{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>141</td>\n",
       "      <td>920300</td>\n",
       "      <td>37141920300</td>\n",
       "      <td>9203</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>236115472</td>\n",
       "      <td>605423</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-78.15648 34.67909, -78.15458 34.680...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>141</td>\n",
       "      <td>990100</td>\n",
       "      <td>37141990100</td>\n",
       "      <td>9901</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>133298393</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-77.71433 34.29117, -77.71406 34.291...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>071</td>\n",
       "      <td>031600</td>\n",
       "      <td>37071031600</td>\n",
       "      <td>316</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>22744042</td>\n",
       "      <td>66304</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-81.32560 35.26363, -81.32556 35.263...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>071</td>\n",
       "      <td>031800</td>\n",
       "      <td>37071031800</td>\n",
       "      <td>318</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>6071060</td>\n",
       "      <td>8149</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-81.25719 35.26613, -81.25585 35.266...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>155</td>\n",
       "      <td>961801</td>\n",
       "      <td>37155961801</td>\n",
       "      <td>9618.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>103973789</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-79.31012 34.59978, -79.31002 34.599...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        37        141    920300  37141920300     9203  Census Tract   G5020   \n",
       "1        37        141    990100  37141990100     9901  Census Tract   G5020   \n",
       "2        37        071    031600  37071031600      316  Census Tract   G5020   \n",
       "3        37        071    031800  37071031800      318  Census Tract   G5020   \n",
       "4        37        155    961801  37155961801  9618.01  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20   AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  236115472     605423  ...       12        0       12        0   \n",
       "1          S          0  133298393  ...        0        0        0        0   \n",
       "2          S   22744042      66304  ...        0        0        0        0   \n",
       "3          S    6071060       8149  ...        0        0        0        0   \n",
       "4          S  103973789          0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        6        0        0        6   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-78.15648 34.67909, -78.15458 34.680...  \n",
       "1  POLYGON ((-77.71433 34.29117, -77.71406 34.291...  \n",
       "2  POLYGON ((-81.32560 35.26363, -81.32556 35.263...  \n",
       "3  POLYGON ((-81.25719 35.26613, -81.25585 35.266...  \n",
       "4  POLYGON ((-79.31012 34.59978, -79.31002 34.599...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/nc_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 2672 popn tracts for NC\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"NC\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population NC voter-age population\n",
      "state pop= 10439388 VAP pct Hispanic is  0.0888169475318448\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP NC\n",
      "total state pop= 10439388 , VAP pct Black is  0.2009838261926679\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "245 4272 0.01240012275299989 nonPolygon tract no, pop, area\n",
      "3.414691650000176e-05\n",
      "2.6396099999900555e-07\n",
      "5.143744999998769e-07\n",
      "1.6841599999901352e-07\n",
      "9.20251499998282e-07\n",
      "0.012364108833499891\n",
      "1554 2305 0.09079886094049996 nonPolygon tract no, pop, area\n",
      "0.004141476349500224\n",
      "0.08665738459099974\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb68f44b-eb19-4993-b856-cfa9c855b2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counter = 0\n",
    "for geom in tractGeom[605].geoms:\n",
    "    if counter ==1 :\n",
    "        fixedGeom = geom\n",
    "    counter +=1\n",
    "tractGeom[605] = fixedGeom\n",
    "plotPoly(tractGeom[605])\n",
    "notPoly[606] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LTl3PIGDnjj1sf3EnRw7v565dQlYB9VENUA2GLPdqfPjvP/zG2LG9D3U5EgBLC6/wxdxPud7dAMB3dr2ZT/74/lO30Ziz0JiGbrTUExwbVA2XX6qqjwza7CngPWOt6Fhdcu1Klr1pBc+t38BvHv0NDzrPMcev43UvLCa6tBaJOMQvqD8hQKdC4UCGI3duKV21A2ixHJICkQVV1NyylNi5taP6KySVSvGBD3yA73//+9x999187GMfIxo9xbDLpR+CQ5uofvo7/GN4Cw/7l9PT1cTtv32Z5qoYzVUxLppdhd9zlEMv72Dzlp08vK2bjV41bZHG8k6aOHYV6lyvG8Qt19VFxAFxBt4XikNVJMzH33QOoh5hP8tX2/bixvIkQt0k/Swpv3/gva7QT1izJP0sc/Jt3ND3OP2RGOvqbmHj44e44i03nPkHb8xZalRfxoqIC2wAlgDfOvnMXUT+E/iZqv50iLK7gU5K52jfVdXbhznGrcCtAPPnz3/d3r17T7MpJyoWizz77LP89te/IefnmePXscJbSIvWEmosXb+tvlJ9/QISlzaN6Vinq+POVnJbj56wLNSUoOot84mdW4sTO/2vTnbs2MHdd9/N0qVLef/734/jnKIz84s8d/ufsfjQQ9RIH0V1eSY4n0eDS/l1cClHvTQrup7jleRCemPVJCTHAu8oK+J5FjYlqE64JJ0CKxbGSDg5KGSg0Af5DBR6B033nTjv50fVlgChz42TcRP0nXcTs6/8DD/6H/+D6uZZfOAr38CxK3KMeY1xu+pGRGqA+4BPq+rm8rLbgJXAu3WInYnIbFU9ICJNwNpy2d+e6jjjeR19PpvnmSee5KnnnqEv209LrIHL65cxPz6L3OYjpK6aQ82Ni8flWKOlxYC+5w7Tdd9OQg2lTsfryJ6wTfzCesJzUkjYRQs+oYYYoaYkbqp8ff4Ql3Q+/fTT/PKXv+T1r389b3vb24Y8dn9PN4d37eTQy9tpe3k77HuGWbqHxelOGqN9Q5YZkROGaAoix17JE+ej5WWR9KB1J88f33bdPXtpfeLQwO7rZz3Bwe3P8ZGvf5OGeQvOrI7GVLhxu+pGVbtEZB2wBtgsIh8FbgKuGyrky2UOlN/bROQ+YBWlL3cnRTQe5arrr2b11Vfy/PPP88QTT3D//t/S0tLChU4jF/Q2ol4wqcM5EnZIXdFC6oqWgWX53d3k9/bQ+9g+NO+TbT1CtvXIMDsAJx5CIi7qB7hVURKXNHL561dy5MgRnnzySRoaGrjogvPLob6Dwy/v4NCunfS0Hx7YTW3LHFJNb2CX9w6ea0+TwOe6+v+Xue529i66lXmLWnBi6ZPCe4hwDkXG9fN58wcvGAh6v7CD/Vuf4co//LCFvDFnaDRfxjYCxXLIx4FHgK8DHvD3wJtVtX2YsknAUdXe8vRa4G9U9aFTHXMiH4HgeR6bNm3i8ccfp7Ozk+ogwcX+fC5oWUpiThWxc+uIX1iPFn2CrPea8k4qcvxa/AmmXoAWfPxMkSBTxO8rlN4zRYK+IsUDGQqv9J5Q5tAVR3l8yya6Cx7xV7YT6i+tr25qpvmcc2lasBhxmjnalmRfa4ZCzieaCLF4RSPnvK6JuefX4rpT+x3GYz99iS1PHECDLPmeH9O4oJkPffXvccfj+T7GVKgxDd2IyCXAnYBL6bHG96jq34jITkqXXB477XxKVT8pIrOBO1T17SKymNJQD5T+erhbVb86UoUnMuiP8X2fFx5bz1ObnqWtp4M4EZb5c7moZglz3ncxbf+8cchyydUt1L7z1FeoTDSvWKRj724OvbyDQ7t20P9yB6tCx4dqclrgwehz9Ememy55A4uuuZSOfR47n2tjzwsd0zLcAbyCz7q7trHt6dLZfKHvIdR7iQ/97T/QvOicKa6dMdPbWXVn7OlSVXbv3s2TTz7Jjh07EIVmrWG+38CS+vnULZ9Fvq+PaDJJbu1houdUU/uec3HKz6hRL8DvKbxmv06stH6svEKRV57aRnvrbvo6DnO0fR99PW2IKoJDOJqgqq6FdG0LCzvnIgj5sM8h/yhrI5u40JtHV2bJtA33Qs7j6IE+nnt4L/u2HsUrlK5AWn2zy7offYMr3vU+3vj+j0xxLY2Z/izoR+ngrv28uH4jOw/spq2rdNm/k8vi9vcgXpHLa64jSYyYhokTYdbNy+j7zz3gv/YzlLBDy1+vPuGa99Pl5Qu8fNtakqETHxZcwKNXsmQkN+g9R0ay9EqOghwfcrqiuJToeZdNq3A/JtdX5If//QmC4PjnN2txFdf+0Tnc8zf/lWg8wYe/9k1C4bF3mMZUOgv6M9DZ2cm2bdt48rFH6c5mh33IVkgdYkRI1VSRiMZJRGOEDnmE+iG2pAac0iONhWPPnj9WctA4v5wwx7ENM729bDu4i6gvxBtqyPoFerO9FLwT/4IIuSGqEmnSiarSe7KKqmQV6XiKc688n3B4eo5tB4Hy+5/v5IVf7RtYpkGWYv8jBN4uPviV/0PL0vOmsIbGzBxnzbNuxlNtbS2rV69m9erVABQKBfr6+ujv7yeTydB7sJO+TB99uX5yFOjL9tPf309Hdyd9fgY/HMDYbgUoEciEoDGcoq6xjsU1i6mpqTnhlUgkZswjHgZzHGH1zYvZuPYV1D+Il99EUNgGBJx35S0W8saMEwv6UYpEIkQiEWpryw9BO0UGqSpevli6s3/QowEGD1Ec37j8IyXHZga97Vz/Io/86B+4/MY/5U3vv37sjRgHvh/gFwKKBR+/GOAVAryij+8pgRfgewG+X5oOfCVZE6V+TpKejhyP/mQr7a90M2ux4LoZirmjHNi+j6C4Cw2OAGHOWXk1l665kQUXnzvVTTWmYljQTwARIRwb+7Xluf4sFPqorp+aJ2yqKg/80wu8sqV0F6/jyGs6q9IvaeVRzYJm0SCHan95urysPK1BL2iGvc8N3oODuM2suOGPueKdbyNVk568BhpzlrCgn8YynaWnVqYbayZk/xooB1/uom1vL4Gv9B7Jkc1kKeYy9HR00tN+lGI+UwppzdIwN0RQ7KdYyODl+yjm+ijkMmjgD7l/xw0RTaRxwwmQJA1zF9OydD7VTbOobmymqqmZVF2dPdLAmAlmQT+N7dtS+q3VJ+/9Ba3ranHcMG4oghsO4YQiOKEwoVAENxQmUROndlYa1w0RjoUJRUIEXpF8tkDb3k52rt9LKFIk291Ntrcb3+unv6cHgn5UcwNn5Ghx6MqI0HM4TTxdRbK6inh6AfGqKuLp0itRVT0wHa+qIl5VTTgam5HfHRhTaSzopzE3UhrGOLT9EQ6NsO3pctwo4WiSUDRFVeMsqhpqSdXWnBDUA8GdriKWStmZtzEzlAX9NPahv/kw+dx78fMFioUCXj5PsVDALxQoFvJ4hSJesUDf0X56jvQS+D5BEOAVigS+z77NvyFV18KCi1cx65xZVDfVlUI8VUUoMr7PpzHGTF8W9NNcNBaF2Jn+rN+Hx7UuxpiZafrcJmmMMWZCWNAbY0yFs6A3xpgKZ0FvjDEVzoLeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4SzojTGmwlnQG2NMhbOgN8aYCmdBb4wxFc6C3hhjKtyIQS8iMRF5RkReEJFWEflyefk3ROQlEdkkIveJSM0w5deIyDYR2SkiXxjn+htjjBnBaM7o88C1qrocWAGsEZHVwFrgIlW9BNgOfPHkgiLiAt8CbgCWAR8QkWXjVHdjjDGjMGLQa0mmPBsuv1RVH1FVr7z8KWDuEMVXATtVdZeqFoB/AW4eh3obY4wZpVGN0YuIKyIbgTZgrao+fdImHwN+OUTROcC+QfOvlpcZY4yZJKMKelX1VXUFpbP2VSJy0bF1InIb4AF3DVF0qF+G1qGOISK3ish6EVnf3t4+mmoZY4wZhdO66kZVu4B1wBoAEfkocBPwIVUdKsBfBeYNmp8LHBhm37er6kpVXdnY2Hg61TLGGHMKo7nqpvHYFTUiEgfeArwkImuAzwPvUNX+YYo/CywVkUUiEgHeD9w/LjU3xhgzKqP5cfAW4M7yFTQOcI+qPiAiO4EosFZEAJ5S1U+KyGzgDlV9u6p6IvIp4GHABX6gqq0T0xRjjDFDGTHoVXUTcOkQy5cMs/0B4O2D5n8B/GIMdTTGGDMGdmesMcZUOAt6Y4ypcBb0xhhT4SzojTGmwo3mqhszyfLFAk+s38CrezvoPpijWPRI18RZ0DKHlqYGmprqSNfFSKQjiDPUPWnGGHOcBf00kusrsuWJA/zmly8SySWAJIVQQBByye6Os/O5XnbSC+wGQEUhWcRJB4RSSrgaqhsTtLTUsXDeHJqb63GsIzDmrGdBPw10He5n06P72PrkQbxCQFv1PjbPf4LD6d08+fHHcR2XXV272HpgO+1tXTy/+0V6j+YI5xLEsimSfTWkjtaQKtTSr8pBjvAcR/DFIx/PEKTz1DWnuHDxUlpa6qlqjFPdECcUcae66caYSSBDP7lgaq1cuVLXr18/1dWYcJu37eDx+14i2JMAB5xzM7jLu/mnV74xsM1TH3yKZDg57D5UlUJQIOfl6C/0s+/QIfYfaKPjUA89HTkKnQo9EaJ9aaJ+/ISy2dqj/Nl/v4l0XWzC2miMmRwiskFVVw61zs7op8je1iM89v/tIY/SOudhWmc9QTbSC6+cuN2Ozh2saFox7H5EhKgbJepGqY5W05JugaWv3W770e2s3fYofUeKSE+E1l07WX7wau79+npu+vRyGuamx7eBxphpw87op8CW3x1g3V3byFZ18cyl9/Lj93x/yO0EIeJGxv34qsr7HngfuTZ4947PUMh53PDJi5l3ft24H8sYMzlOdUZvl1dOIlXlmQd289hPXqKrfj//seybJGsiA2fkJ78mIuQBdnTtYOvRrVx47jnc8levI10X44F/eoHtzxyakOMZY6aWBf0k8f2Ax37yEs8+sJvCknbuXfKPXNByHu9e+u5Jr8st998CwCWNl5Cui/Huz13GrMXVrP3BFp57ZC/T8a88Y8yZs6CfBIWcxy++vYmtvz/IyhsXsu2SxzinbjHfvf673LT4pkmvz+cv/zwAf/f03/HXv/treqWbd/zlCpa8roknf/4yj9+zgyCwsDemUtiXsROsryvHg99+kY5Xe6m+vp/9529k+/PbWdk85FDapPjwsg/zB+f8Ad/b9D3u3HIn/77z37l63tWwGJp6L+LFx2DzwVb+4rPvmrI6GmPGjwX9BAryBX72l/eRTTRzyaZvs/3gFr7+ztK1673F3impkx/4dBe6KfgFPrLsI9TEavjZtp+xo3MHrrjsXbyXizpuoGHnvJF3ZoyZESzoJ5BX8ChEawDYueQWQrnFXCJH2BQ8w5qFawDI+3l6C73UxepwZOwjacWgyEd+8RFaj7TSFG8i7IYH1uX9PJ25Tnz1CftR0rk60vl6GvLnks7Xkc7XUZWrp6F/Lv2xrjHXxRgzPVjQT6BIOsGHv/J6Xt5wiN2tSQ7saeYNv4dLIjeyqbWX9c4dZJ0+im4eDXkkkjHSySSpZIJEPIaEIRoJEwq7JGMJ0vEk+U4llBBCg+6hKvpFCn6BglfAcVza9vbSpPOhV2gMzeP80MW4fXEifQki/SmcTBTNndSphAKkykMaPTTdxUUXNUzuh2WMmTB2Hf0kymYKvPjsHh5/egNacAn7UcJeBCm6+AVFCw5OMDGPJXBDDun6GFUNMdL1carqY6X5+jjp+hjxdJjyT0IaY2YguzN2moinIqy65lxWXXPusNv4XkA+W6Q704vvBfTnsuQLRTLZPnr7+ziyuUis3iHe6AwEc8gJEXbDhN0wgfp4gUchKOAFHktnLaalpcGedGnMWcyCfppxQw6JdJREOjr0Bm+a3PoYY2Y+u47eGGMqnAW9McZUOAt6Y4ypcBb0xhhT4UYMehGJicgzIvKCiLSKyJfLy99bng9EZNj7+UVkj4i8KCIbRaTyrpk0xphpbjRX3eSBa1U1IyJh4AkR+SWwGXg38N1R7OMaVe0YQz2NMcacoRGDXkt3VGXKs+HyS1V1K2A32RhjzDQ3qjF6EXFFZCPQBqxV1adP4xgKPCIiG0Tk1lMc41YRWS8i69vb209j98YYY05lVEGvqr6qrgDmAqtE5KLTOMaVqnoZcAPwX0RkyFt+VPV2VV2pqisbGxtPY/fGGGNO5bSuulHVLmAdsOY0yhwov7cB9wGrTueYxhhjxmY0V900ikhNeToOvAV4aTQ7F5GkiKSPTQNvpfQlrjHGmEkymjP6FuAxEdkEPEtpjP4BEXmXiLwKvB54UEQeBhCR2SLyi3LZZkpX6bwAPAM8qKoPjX8zjDHGDMceU2yMMRXgVI8ptjtjjTGmwlnQG2NMhbOgN8aYCmdBb4wxFc6C3hhjKpwFvTHGVDgLemOMqXAW9MYYU+Es6I0xpsJZ0BtjTIWzoDfGmApnQW+MMRXOgt4YYyqcBb0xxlQ4C3pjjKlwFvTGGFPhLOiNMabCWdAbY0yFs6A3xpgKZ0FvjDEVzoLeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4UYMehGJicgzIvKCiLSKyJfLy99bng9EZOUpyq8RkW0islNEvjCelTfGGDOy0Ci2yQPXqmpGRMLAEyLyS2Az8G7gu8MVFBEX+BZwPfAq8KyI3K+qW8ZedWOMMaMxYtCrqgKZ8my4/FJV3QogIqcqvgrYqaq7ytv+C3AzYEFvjDGTZFRj9CLiishGoA1Yq6pPj3L/c4B9g+ZfLS8b6hi3ish6EVnf3t4+yt0bY4wZyaiCXlV9VV0BzAVWichFo9z/UKf7OswxblfVlaq6srGxcZS7N8YYM5LTuupGVbuAdcCaURZ5FZg3aH4ucOB0jmmMMWZsRnPVTaOI1JSn48BbgJdGuf9ngaUiskhEIsD7gfvPsK7GGGPOwGjO6FuAx0RkE6XgXquqD4jIu0TkVeD1wIMi8jCAiMwWkV8AqKoHfAp4GNgK3KOqrRPREGOMMUOT0kU108vKlSt1/fr1U10NY4yZMURkg6oOeU+T3RlrjDEVzoLeGGMqnAW9McZUuNE8AsEYM009d7Cb9/3gabxCAIBI6W51xxXckIPrCuGwSzzqko6FScdC1MTD1CciNCQiNKcizElGaUnGqEuGqYqFSUTcke54NzOMBb0xM9h/bm+j2FuktjlBVVWUQMELAjxfKXgBfjEgV/DozRQ4WMigXoAEI+xUQEIObsTBjbgkYiGqUxEa01GunVPLBQ0p5tTEmV0TJxm1CJkJ7F/JmBlsX08WgHs/dgWLqxMjbp/zAw5l8+zPFDjQl+NgJk97f5GO/gJd/UWyBY9s3iOX98nnfXIFj/5+j67OHLtzPs8+d+iE/VXHw8yuiTO7OlZ6r4kzuybGnJo4LTVxmtNRQq6NEE81C3pjZjA/76MCeXd028dch4WpOAtTcaD6tI713ud28Pihbhbh8t9mNdLek+dAV5YDXVn2d2VZv7eT7mzxhDKOwKyq451AS7kTmF19vFOojodtqGiCWdAbM4NFfSDssK0vzwWpkc/ox+KHlyzm+sI2dmcL/F2umw1XX/iabTJ5j4Pl4D/YnRvoBA50Zdm4r4uHNuco+CeOHSUi7kBHMKcmRkv1iX8ZzKqOEQ2NsiczQ7KgN2YGy2Y9NOKQ8UcaeB+7pOtwcTrB7myB/fki+SAg6pw4LJOKhljanGZpc3rIfQSB0tGX50BXbuCvgYHp7ixbDvTQkcm/plxDKsqcmkF/GVTHBr4nmF0Tpz4ZwXHsr4LhWNAbM4P1Zoto2KHf9yf8WArc39Y1MP/Zra9wXjKGK0K/H9Dp+XQVPbqKPumQy/nJGK+vSXFhKkZVqHQlj+MITekYTekYF8+txuG1v2mRK/oc6j4W/rkThoe2H+7lsW1t5IondmziCJFEiFgyzOzaOG9ZWM/qubUsbUrRmI6e9UNDFvTGzGA7uvoh5tA3CWf0jgh3XryIH+3vICzCsz193FcOfgeoCbvUhEJUhVxeyRV4oL1r4JnkKdehPhwiJELG9+nxArJBQFiEdMgh6bpUh1zCIoQdwRUQBE8UpxaOpiIU5oRoL8Tp83woKrGCz3xc/H6fYn+RQr9Htq/AlpePsvWlI/xT+djxqMv5zWnOa06zpCnFuc1pljanmFUVO2s6AAt6Y2aw3mwRTce4ofH0vlg9U29rqOZtDceP1e8HOEDUkdeEZnfR45nuPnb259mfL9BZ9CmqknIdqkIuKdclFwT0eD79fkC35+OpUgwUT5UAJSKCp8rSZJSQCE2REEsTMS6rSnB+Mk5oiOGag7kCP9zVxu/2HWVbW4bengKt/Tl2t/bR9ezxL4tT0RBLmlIsbUqxtDnF0qZSRzCnJl5xw0AW9MbMUL4foMWAlQ1pzk/Gp6QOiVNcOlkdDnF9QzXXT2J9AFpiEf6fZXNh2Vx6PZ+lj79ID9ADRIoBxd4iH6+uxu3z2NGWYd32dv51w6sD5eNht9QBlMP/WEcwtzaBO0M7AAt6Y2aoQ/0FRKE2GZnqqkxb6ZDL06sv4NVcgd3ZAnfu7+DFcJY7yHHh7BjLzm3hqmScRKAcPpqluzPH9sMZjnZmeWJnBz9/bv/AvqIhh3Maj3UAKZY0pTm3OcX8usS0v1fAgt6YGWpfbw6Aegv6U1oQj7IgHuXKWvhASx137u9gT7bAjv4cjx7p5V8PdR7f2AFaQtCSBtJcm06yKHA5dLSf9qNZjhzNsnZnO/+x8fgP5UVch8WNyfIwUHqgI1hQnyQSmh4dgAW9MTPUi0f7APh162H+W6A4jhB2HEKO4DqlLzVDjkPIFUKOEHEcQi4D20Sc0rNwIo4Qdp3yeiHqHlt/rIxD1C3tO+I6RMplXHntuPx054rwsbkn/ib1/lyBoiqq0Ov7dBQ8PvPSK7QXPB7tLX3GRCE0O4zXEgaqwAuIZ32aChDr98n0FnlqbycPbjo48AV0yBEWNSRZ2lw6+z82BLSoITnp9wVY0BszQ+3I51Ggc3+Ge/dnJv34KoAAIkj5nfIyGbRMRBDn+DJx5PjD1watcxzBKa93BuYFxykFtOOUXsem3ZOnHcF1wB3U2bkihNzS+7HOz3WFkBzvAEODOshQudP7TLqGsCsIpTYlQy5RV/AFvrLrIPFQmJbGJK/mC+wtFMk1h0GSECjS7+H0e/xpXR17O/rYerCXhzYfIij3AK7AnBpYUFtgcb3LebOqWb7wEs5pqiIWnpgOwH5hypgZKucHvNiZAU9xFPJBgBcoBT+gGChFXykGpWlv0DJfAwq+4gVaegBaefmxeV+h6Af4QWmZX34Nnva1tG0QMDDvB0pQXh/o8fcgKN0opVpaf2zZ8fnStJ40rQqUz7RLy47PlxZSfpWnp6mQI3jB6Cu452s3ntFxTvULU3ZGb8wMFXMdLm+omupqTBtBuaMq+EreDyiUO7mCH1DwA7wA8n4w0PkV/eMdXtEPKJTLlzrI8nS54yr6QbkjPLGD9AZ1gl6g9Hk+rT1Zcn7AdXVplsSjA9vs3vs9AnVxQ7V4AThuFfmi8kpXhH29s/A1RMSdmB7Lgt4YUxEcR4g4LpEQpKa6MkP6+yk78vT4StgYY8yEsaA3xpgKZ0FvjDEVzoLeGGMq3IhfxopIDPgtEC1v/2+q+iURqQN+BiwE9gB/qKqdQ5TfA/QCPuANd/mPMcaYiTGaM/o8cK2qLgdWAGtEZDXwBeDXqroU+HV5fjjXqOoKC3ljjJl8Iwa9lhy77S5cfilwM3BnefmdwDsnooLGGGPGZlRj9CLiishGoA1Yq6pPA82qehCg/N40THEFHhGRDSJy6ymOcauIrBeR9e3t7afVCGOMMcM7rUcgiEgNcB/waeAJVa0ZtK5TVWuHKDNbVQ+ISBOwFvi0qv52hOO0A3tHXbHx0QB0TPIxJ5q1aeaoxHZZmybXAlVtHGrFad0Zq6pdIrIOWAMcFpEWVT0oIi2UzvaHKnOg/N4mIvcBqyh9uXuq4wxZ2YkkIusr7TsEa9PMUYntsjZNHyMO3YhIY/lMHhGJA28BXgLuBz5a3uyjwH8MUTYpIulj08Bbgc3jUnNjjDGjMpoz+hbgThFxKXUM96jqAyLyJHCPiHwceAV4L5SGaoA7VPXtQDNwX/mZ1SHgblV9aALaYYwxZhgjBr2qbgIuHWL5EeC6IZYfAN5ent4FLB97NSfF7VNdgQlgbZo5KrFd1qZpYlo+j94YY8z4sUcgGGNMhbOgN8aYCndWBb2IrBCRp0RkY/nmrFUnrZ8vIhkR+dwI+/mciKiINExsjUc21jaJyDdE5CUR2SQi9x27wmoqjUOb6kRkrYjsKL+/5v6OyTZcm0RkVXnZRhF5QUTedTrlp9JY21Te9tMisk1EWkXkf09e7Yc3Hu0qbz9tcqL0W4xnyQt4BLihPP12YN1J6+8F/hX43Cn2MQ94mNINXQ0zvU2ULnkNlae/Dny9Atr0v4EvlKe/MJ3bBCQGff7H7kcJne5nMkPbdA3wKyBanm+a6jaNR7vK66dVTpxVZ/SUHsdw7Ec2q4EDx1aIyDuBXUDrCPv4B+CvmD4/RzymNqnqI6rqlWefAuZOTDVPy1j/nabjc5iGbJOq9g/6/GMM///VsJ/JFBprm/4c+Jqq5svlhrzpcgqMtV0w3XJiqnuaSe6pL6B0zf8+YD+lW4YBksCTlH5q8n8x/JniO4Bvlqf3MA166rG26aR9/Sfw4ZneJqDrpPnO6dqm8rorKHVcGeBdp1t+BrdpI/Bl4GngN8DlU92mcWrXtMuJivtxcBH5FTBriFW3Ubru/7+q6r0i8ofA9ynd6ftl4B9UNVO+uWuo/SbK+3jrhFT8FCaqTScd4zbAA+4at4qf+ngT3qbJdoZtQksPCbxQRC6gdHPiL1U1d9I+/ny48hNpgtsUAmqB1cDllG7AXKzlhJxIE9WuqcyJU5rqnmaSe+pujt87IEBPefpxSj3vHqALOAp86qSyF1Makzu2nUep1581U9s0aB8fpXSmnJjqf6PxaBOwDWgpT7cA26Zrm4bY7jFg5ZmWn2Ftegi4etD8y0DjTG7XdM2Js22M/gDw5vL0tcAOAFW9SlUXqupC4B+Bv1XVfx5cUFVfVNWmQdu9Clymqocmq/LDOOM2AYjIGuDzwDtUtX9SajyyMbWJUTyHaQoM2SYRWSQiofL0AuA8SgExqvJTbKxt+vdyOUTkXCDC9Hgy5Bm3a7rmRMUN3YzgE8A3y/9YOWDY5+MfIyJ3AN9R1fUTXbkzNNY2/TOln4lcWx4OeUpVPzmB9R2NsbbpawzxHKYpNlyb3gh8QUSKQAD8hap2wGvadNqfySQYa5t+APxARDYDBeCjWj4tnmJjbde0Y49AMMaYCne2Dd0YY8xZx4LeGGMqnAW9McZUOAt6Y4ypcBb0xhhT4SzojTGmwlnQG2NMhfv/AahBgdHVxcVMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for GA)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 47, 194, 491, 725, 726, 809, 1379, 1401, 1402, 1825, 1994, 2108, 2258]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "lakeTracts = [9999]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if x > -78.2:  #NC outer banks\n",
    "            if lakeTracts == [9999]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if x > -78.2:  #m==99940 or m==999909:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    thisTract = \"interior\"\n",
    "                    \n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KUEqeFoIULgTg3EH0pUsHsOiDfeQXWLcTxpjGeW59IRdktUuZLiVqS+lCAHD31UPo2i6D+17YRD0dpBpjzDkKS0/z/seH+dzYPinTpURtKV8IMlu34p6pF7J2bynPrS/0Oo4xJsm8sL4IgBvGpuZpIfBBIQC4eXw2Y/p24r9WbOVEWUX9KxhjDE6XEkvWFjChf2f6dW3rdZxm44tCEAgIP7l+BMXHy3jw9Z1exzHGJImNBUfZWXyCm8efMyR7SvFFIQAY168zsyZk8+d3dvHeLuud1BhTvyVrC0hPCzB9dGp1KVGbbwoBwL9NH07fLm352v/lsuPAca/jGGMSWFlFJS9sKOLai3rSsU0rr+M0K18Vgk5t03nsyxPJaBXkS3/5gAPHzngdyRiToN7YWkzpqbPcPD51LxKH+aoQAPTt0pa/fOliSk+V86W/fMDxM2e9jmSMSUCL8wrpnpnBp4dkeR2l2cVcCEQkKCLrRGS5O32/iGwVkY0isiw8SH2tdfqKyBsiskVENovI3XHM3mgj+3TkD3MmsOPAcb7x+FrKK6xjOmNMtYMnyli9rZgbx/UhGEjNZwciNeSI4G5gS8T0KmCkqo4GtgPzo6xTAXxfVYcDk4BvisiIxoaNpyuGZvFfN43inZ0H+eGSjfawmTGmyvPri6gIKTdPSO27hcJiKgQikg1MBxaG56nqSlUN35S/BjjnHVPV/aq61n19HKeQJMwJt9k5ffnXa4aydF2h9UdkjKmyJK+AUX06MrRHptdRWkSsRwQPAPOAus6h3AW8dL4NiMgAYBzwXh3L54pIrojklpSUxBir6b49ZTCfn9iXh974iL9Zl9XG+N6W/cf4cP8xZvnkaABiKAQiMgMoVtW8Opbfi3MK6InzbKM9sAT4rqoei9ZGVReoao6q5mRltdzFGRHh5zeMZMqw7tz3/CZWfXigxfZtjEk8S/IKaBUUZo7p7XWUFhPLEcFkYKaI7AYWAVNE5HEAEbkTmAHcoXWcZBeRVjhF4AlVXRqX1HGWFgzw4O3jGNWnI996ci0vbCjyOpIxxgNnK0M8t76QKcO607ldutdxWky9hUBV56tqtqoOAG4DXlfVOSIyFbgHmKmqp6KtK05XfX8Gtqjq/8Yxd9y1TU/j0S9dzKg+HfnOU+v4zxVbCIXsArIxfvLW9hIOnihP+S4lamvKcwQPApnAKhFZLyKPAIhIbxFZ4baZDHwB5yhivfs1rWmRm0/X9hk8+bVJzJnUjwVv7WLeko1U2JjHxvjGkrUFdGmXzpUXdvc6SotKa0hjVV0NrHZfD66jTREwzX39DpBUN+GmpwX4+Q0j6doug9++toNT5RU8cOs40tN89+ydMb5y5GQ5r35YzB2T+vnu//cGFQK/EBG+d81QMlun8R8vbuFkWS6PzJlAm/Sg19GMMc3khQ1FlFeGmD2hr9dRWpy/yl4DffXTg/jvm0bx1o4S7nz0fY5ZdxTGpKxn8/YxolcHRqTocJTnY4WgHrdN7MfvbhvH2r1HuONP73H4ZLnXkYwxcbZl/zE2FR5jdo6/LhKHWSGIwfVjerPgixPYfuA4t/7xXeu11JgUs9h9diCVh6M8HysEMZoyrAeP3TWRotLTzHrkn+w9FPWOWWNMkjlbGeK5dYVcPbwHXXz07EAkKwQNMGlQV5742iSOn6lg9h//yZ5DJ72OZIxpote3FnPoZLlvTwuB3TXUYGP7duLpuZdw4x/+wfLffovL2hUy5rTTfdIngZ4cCzgXmkRDgCIoKM53ASWASgBFCBFERVAJOi0lUL084jUR01WvJQDuegQCqATBXV49LWggCBJAwu0lAIEgiCDu5wCtusG3+k5frXot7uzadwELiNRo607WXLeqea15EW0FiVinunnt7WjENqR2ZhE3Zs1pZxtS40cQCdRcp2pTgaotaq3tSY3tSs23QwLhd8BtXitT1XakejvhHIGAu04gYtvi/pu57d0vwW3v7jMQsSzc3skiSCC8L3HXcfcTEPe1IIGAkykQzhbeZ8BdRq12AQJuGxWq2gfc3y9nOwFnHyIEqrbl7tPdTyDgbF/cbdbM2/IW5xWQlZnB5T4Yd6AuVgga4cKemXzzqsFc/s5WRp3+sGp+ebAtZ4IdCP+R1Ko/lhIuCYiGEELO9xqvlYCWO9+pdNaoWl49L+DOCxAiENkWJYi7TEM4ZcNtV/VaCYg9LW0SV0iFUMT/L5H/7zi/wTXnqYRfS1XRV6gxL/yBQt3/cyqkFRXSikpJo0LSua28DSMn/Jy0oH9PkFghaKRvXjUYLl0Jz34Jdr4Kl8+j31U/op9Hn2pipaEQoVCIykq3B/GqLqIUdf83qzFPa05Xb6jmPK16ANud1sgnssNtam9H3eKoNdqqaq1ckfuLXORmVjdn1X5C4VeoUtWmah2t3nK4i6zwPiP3Vb0sVBVbI6ar21ZniHwPq2OG38dQxHarf07VkJvZzVmVJQQhJRQ5XWu5hsKvnQ8O1dus3oeqIlpZ4+cLb0tDzp9drfFzhPcRqvr5qpeHarStsTyivVTtKyIzofCbhroffqq2Q82fo3o+EW3D72HNdcTNVb2ciGWR+wfRSgKhswRC5QRCZ+lTvothgRIuGtURP7NC0BQZmfD5RbD8e/DWr+DoPrj+d5CWuBecJBAgGAgQTLN/emN4+DIIZtNz0Civk3jKv8dC8RJsBTN/D1f+CDY8BU/OhjNHvU5ljKnPJ/lwIB/G3O51Es9ZIYgHEbjyHrjhD7D7HfjLNDhmXVkbk9DWPwWBVjBqltdJPGeFIJ7G3QG3PwNH9sDCq+HAZq8TGWOiqTwL+c/A0GuhbRev03jOCkG8Df4M3PWSc9Hq0amw602vExljatv5GpwsgbF2WgisEDSPnqPgq69Chz7w+E3wzgMQsnENjEkYG56Etl1h8DVeJ0kIVgiaS8ds+MorMGw6vHofPDELTpR4ncoYc+owbHsJRs1O6Dv8WpIVgubUuiPMfgxm/Ab2/AMemQy7Vnudyhh/27wUKsthzOe9TpIwrBA0NxHIuQu+9rpTGP7vc/DazyH8QJcxpmWtfwq6j4BeY7xOkjBiLgQiEhSRdSKy3J2+X0S2ishGEVkmIp3qWO9RESkWkU1xypycelwEc1c7dxa9/Wv463Q4WuB1KmP85eAOKMx1jgYSvBeAltSQI4K7gS0R06uAkao6GtgOzK9jvb8CUxuVLtWkt4MbHoKbFsKBTfDwZNj6otepjPGP9U86nQuOvsXrJAklpkIgItnAdGBheJ6qrlTV8PmNNUDUPlxV9S3gcBNzppbRs+Ff3oLO/WHR7fD8t5wLWMaY5hOqhI1PwwWfgcyeXqdJKLEeETwAzAPqugfyLuClpgQRkbkikisiuSUlPri7pusF8JVVMPlu51PK78fDB3+GChsK05hm8fFbcKwQxtpF4trqLQQiMgMoVtW8OpbfC1QATzQliKouUNUcVc3JyvJJv+BpGXDNz+Dr70DWcHjxX+H3EyD3USsIxsTb7red70Ou9TZHAorliGAyMFNEdgOLgCki8jiAiNwJzADu0HCfvabheoyAL6+AOxZD++5Ob6a/Gwdv/y8c2+91OmOSnyp8+Dz0uxQy2nudJuHUWwhUdb6qZqvqAOA24HVVnSMiU4F7gJmqagP4NpUIDLnGeSJ5zlLoPABe+yn8ZgT87SZ4/09OH0bGmIYrzINDO+20UB2a8hzBg0AmsEpE1ovIIwAi0ltEVoQbichTwLvAhSJSICJfaVLiVCfi9Ff05Rfh22vhsu/B4Y9gxQ/gt6OdO412v+N1SmOSy/onIa01jLjB6yQJSRLxjE5OTo7m5uZ6HSNxqDqfZra/Am/8wnkm4auvep3KmORQUQa/HgqDr4ZZf/Y6TbMRkTxVzWnMuvZkcTIQgW5D4NJvQUYHpzM7Y0xstr0EZ0rttNB5WCFINl0Gwo6VULjW6yTGJIcNi6B9Txh0lddJEpYVgmRz05+cPote/L51bW1MfU6UwM5VzpPEgaDXaRKWFYJk06kvXP1TKFrrjJFsjKlb/rMQqrCeRuthhSAZjb4FsifCqz+BM8e8TmNM4trwlNPLaI8RXidJaFYIkpEIXPdLOFkMb93vdRpjEtOBzfDJRhhjw1HWxwpBsuozHsbOgTUPw6GPvE5jTOJZ/yQE0mDULK+TJDwrBMnsMz92jg7WPuZ1EmMSS2WFc31gyGehXTev0yQ8KwTJLLMHdOoPhz/2OokxiWXXG3DigF0kjpEVgmTXuT+UWh9ExtSw/klo0xmGWk+jsbBCkOw69bfO6IyJdLrUGflv5Cynq3dTLysEya5zf+fx+TNHvU5iTGL48DmoLLPTQg1ghSDZdervfLejAmMc65+CbkOdO+tMTKwQJLvO4UKw29MYxiSEQx/BvjXO0YCI12mShhWCZBc+IjhszxIYw8anAYHRt3qdJKlYIUh2bTpDz1Gw9m8QqvQ6jTHeCYWcLiUGXQEdrav2hrBCkOxE4PJ5zhHBpqVepzHGO3v/CaV7rUuJRrBCkAqGzYDuF8Hb/+OMZmaMH214CtLbw/AZXidJOjEXAhEJisg6EVnuTt8vIltFZKOILBORTnWsN1VEtonIThH5YZxym0iBAFzyTSjZArvf9jqNMS2v/BRsft4Zkzi9nddpkk5DjgjuBrZETK8CRqrqaGA7ML/2CiISBB4CrgNGAJ8XEesPtjmMvMm5XvD+n7xOYkzL27ocyo/bswONFFMhEJFsYDqwMDxPVVeqaoU7uQbIjrLqRGCnqu5S1XJgEXBD0yKbqFq1gfFfdJ6oPFrgdRpjWlae2/Fiv0u8zZGkYj0ieACYB9Q1NuJdwEtR5vcB9kVMF7jzziEic0UkV0RyS0pKYoxlasi5CzQEuX/xOokxLWvPO873sye9zZGk6i0EIjIDKFbVvDqW3wtUAE9EWxxlXtSrmaq6QFVzVDUnKyurvlgmms4DYOhUp1vqijKv0xjTMlSh24XO0UDrjl6nSUqxHBFMBmaKyG6cUztTRORxABG5E5gB3KEa9XaVAqBvxHQ2UNSkxOb8Rt8CJ0ug+EOvkxjTMg5sgoPbbACaJqi3EKjqfFXNVtUBwG3A66o6R0SmAvcAM1X1VB2rfwAMEZGBIpLurv9CnLKbaMKDcJSd8DaHMS0lf7EzEtmIG71OkrSa8hzBg0AmsEpE1ovIIwAi0ltEVgC4F5O/BbyCc8fRM6q6uYmZzflk9na+b3/Z2xzGtIRQCDYtgUFXQbuuXqdJWmkNaayqq4HV7uvBdbQpAqZFTK8AVjQ6oWmYboOdi8bvPgQ9R8Oo2c5zBsakooL34eg+mPLvXidJavYXIhVd83On/6Flc+GPlzu3lNoTxyYV5T8Laa1h2LT625o6WSFIRRntYe5quHGBczvdotth4dWwd43XyYyJn8qzsPk5uPA6yMj0Ok1Ss0KQqgJBGHMrfPMDmPkgHCuER6+Fp78Ah3d5nc6Yptv1Jpw66AxJaZrECkGqC6bB+C/At/Pgyh/BjlXw0KecATyMSWabFkNGRxhyjddJkp4VAr9IbwdZQyF0FroPh8yeXicypvHOnoYtf4cR19sA9XFghcAvtr4Ii++C7Ivhzr9bD40muW1/BcpPOHfFmSazQuAXJw44/RBd/FV7DN8kv/xnoX0PGPBpr5OkBCsEfjHui85zBS/Ng6OFXqcxpvFOlzrXui66ybkpwjSZFQK/CKbBrEfh7BlY8hWorKh/HWMS0dblUFlmfQvFkRUCP+k2BK7/Lex9F9b+1es0xjRO/mKnp90+E7xOkjKsEPjN6NnQfYTzP5Mxyeb4Afj4TefZAYnWy71pDCsEfjR0qvOUcUW510mMaZgPn3NuerC7heLKCoEfdRkIKBzf73USYxom/1noMRK6D/M6SUqxQuBHHdzRQre/bJ3RmeRx+GMo+MAuEjcDKwR+1G8S9B7v3Er6+M3W95BJDpuWON9H3uxtjhRkhcCP0tvBV1+Fqb+Efe/DHy6BvMe8TmXM+W1aAn0nQad+XidJOVYI/CoQhElfh2+979yG9/J8OHPU61TGRHdgszMOt50WahZWCPyuQ2+4cr4zbsGGRV6nMSa6/MUgQRjxOa+TpKSYC4GIBEVknYgsd6dni8hmEQmJSM551rtbRDa5bb8bh8wmXlSdgT2WfAUkAK07eZ3ImHOpOl1OD7oS2md5nSYlNeSI4G6cAejDNgE3AW/VtYKIjAS+BkwExgAzRGRII3Ka5vD8t+DZO53Ou772ujOQjTGJpuADKN1rzw40o5gKgYhkA9OBheF5qrpFVbfVs+pwYI2qnlLVCuBN4MbGhjVxdvqI8z0QdPp2f28BfPi8cwH5yB6oKPM2nzEQMS7xdK+TpKy0GNs9AMwDGjow6CbgFyLSFTgNTANyozUUkbnAXIB+/eyugBZx2xOw8Rl485fwzgOglee26T8ZvvAcpKW3dDpjnM4RNy+DoddC6w5ep0lZ9RYCEZkBFKtqnohc2ZCNq+oWEfklsAo4AWwAonZ7qaoLgAUAOTk59pRTSxBxTgeNuRVCIWf81+OfOGMXHP8EirfAmocg988w6RtepzV+9PGbcLLExiVuZrGcGpoMzBSR3cAiYIqIPB7rDlT1z6o6XlUvBw4DOxqV1DSvQADad4cju+HoPufooPswyOwFq/8LTh32OqHxo01LIKMDDPms10lSWr1HBKo6H5gP4B4R/EBV58S6AxHprqrFItIP5+LyJY2Lappd6T545gvRlx3YBAMvb9k8xt/OnnGuXQ2/Hlq19jpNSov1GsE5RORG4PdAFvCiiKxX1WtFpDewUFWnuU2XuNcIzgLfVNUjTU5tmkf5Cef71P+G4TOdXh41BMF06NDL22zGf3ashLJj9hBZC2hQIVDV1cBq9/UyYFmUNkU4F4XD0zaoaLLo1B8ye8MHf4bxd0J6W68TGT/LfxbaZcEAOxJtbvZksamW3hZufBgO7YC3fuV1GuNnZ47B9leccYmDjT5xYWJkhcDUNOhKp++hovVeJzF+VjUusT1E1hKsEJhzte4IZce9TmH8LH+xc6oyu87ea0wcWSEw50pvX33h2JiWdqIEdq12LhLbuMQtwgqBOVdGBzsiMN758DnnORZ7iKzFWCEw58rItEJgvJP/LHS/CHqM8DqJb1ghMOdq29UpBEcLvU5i/ObIHtj3Hoyy4ShbkhUCc67Rtzg9kv7jAa+TGL+xcYk9YYXAnKtzfxh7uzOO8eGPvU5j/GTTEsieCJ0HeJ3EV6wQmOgun+f0Af/EbDh50Os0xg+Ktzh9WtmzAy3OCoGJrlNfuH2R0xPpE7Ps4rFpfvmLnSFTL/qc10l8xwqBqVv/S2H2Y7B/Iyy63ekN0pjmUGNc4u5ep/EdKwTm/C6cCp97GD5+yxnkvjLquELGNE1hnjMWhj074AkrBKZ+Y26Fqb90+n9Z/l3n05sx8ZT/LAQzYPgMr5P4knXrZ2Iz6etw6pDTK2nbLnDNz7xOZFJFqBI2LYWhn3X6uTItzgqBid1VP3KKwT9+C226wGXf9TqRSQUfvwUni+1uIQ9ZITCxE4Fp98PpI/DqfdCpH4y8yetUJtltWgzpmTYusYfsGoFpmEAQxrlDVi/+MpRs9zaPSW4VZfBheFziNl6n8a2YC4GIBEVknYgsd6dni8hmEQmJSJ2dhovI99x2m0TkKRGxUaiTXZ/xThcAEoSHLobHZjqDjNsdRaahdqyCsqPWt5DHGnJEcDewJWJ6E3AT8FZdK4hIH+A7QI6qjgSCwG2NyGkSSZvOMOtR+P42mPLvcOgjeHoO/HY0vHk/nD3tdUKTLPKfhbbdYOCVXifxtZgKgYhkA9OBheF5qrpFVbfFsHoa0EZE0oC2QFFjgpoE1D4LLv8B3L0Bbn0Cul4Ab/wHfPiC18lMMig7DttfhotutHGJPRbrEcEDwDwg1JCNq2oh8GtgL7AfOKqqK6O1FZG5IpIrIrklJSUN2Y3xWjDNuf979mPO9OnD3uYxyWHri1Bxxu4WSgD1FgIRmQEUq2peQzcuIp2BG4CBQG+gnYjMidZWVReoao6q5mRlZTV0VyYRtO4IrdpCyVavk5hkkL8YOvaDvhO9TuJ7sRwRTAZmishuYBEwRUQej3H7VwMfq2qJqp4FlgKXNiqpSXyBIAybDpufg4pyr9OYRHbyIHz0unOR2MYl9ly9hUBV56tqtqoOwLnQ+7qqRv1UH8VeYJKItBURAT5DzQvOJtWMvhXOlMLOVV4nMYnMxiVOKI1+jkBEbhSRAuAS4EURecWd31tEVgCo6nvAYmAtkO/ub0GTU5vENegq5y6QDYu8TmISWf5iyBoOPS7yOomhgU8Wq+pqYLX7ehmwLEqbImBaxPR9wH1NCWmSSDANRs2C3EedJ5DbdPY6kUk0pftg77sw5d/stFCCsCeLTfyN+TxUlsPCa5wHhoyJVDUusZ0WShRWCEz89R4LdywG1Bnd7InZcHCn16lMoti0GPrkQJeBXicxLisEpnkMuQa+8S589j9gz7vwh0nwyr1w5qjXyYyXSrbBJ/n27ECCscf5TPNJS4dLv+3cSfTaT+Hdh2Dj03D5/4PMnhCqcPonCkV+VTr3lfce63X65KbqDiCk1dNoxKBCTV3eyG2u/T93XOIb4/rjmqaxQmCaX/vucMNDkPMVePmH8NK8+tfpOgQ0FPEV/qNS6+H2qKOlaQK1ibJOjT+W7n/i+Uc70Q26EjJ7eJ3CRLBCYFpOn/Fw1yvOk8cagkCa+xWEQCvn9faX4aPXnJ5NJeB+SfVrBM650STKnSfn3I3iYZuod8ZIxPzwa6nZNjwv8nWd86hnuVQ1qX/fDdhPTHlrrTPoiijvh/GSFQLTskSg+/C6l0+40/kyxrQYu1hsjDE+Z4XAGGN8zgqBMcb4nBUCY4zxOSsExhjjc1YIjDHG56wQGGOMz1khMMYYnxON+ti8t0SkBNjTTJvvBhxspm03heVqGMvVMJarYZIxV39VbdSA7wlZCJqTiOSqao7XOWqzXA1juRrGcjWM33LZqSFjjPE5KwTGGONzfiwEC7wOUAfL1TCWq2EsV8P4KpfvrhEYY4ypyY9HBMYYYyJYITDGGJ9LyUIgImNFZI2IrBeRXBGZ6M6/w50X/gqJyNhY10+AXE9HtNktIusTIZfb9tsisk1ENovIrxIhl4j8REQKI9pNS4RcEdv5gYioiHRLhFwi8nMR2ei2WSkivRMk1/0istXNtkxEOiVIrtnu73tIROJyS2ccMnURkVUissP93jmmHatqyn0BK4Hr3NfTgNVR2owCdjV2fS9y1Wr3P8CPEyEXcBXwKpDhTndPkFw/AX6QaL9f7vK+wCs4D052S4RcQIeI198BHkmQXJ8F0tzXvwR+mSC5hgMXAquBnATJ9Cvgh+7rH8b6XqXkEQHOKN4d3NcdgaIobT4PPNWE9b3IBYCICHBLfe1aMNc3gP9W1TIAVS1OkFzNJR65fgPMI74jzjcpl6oei5hsF8dsTc21UlUr3Mk1QHaC5NqiqtvilCUumYAbgMfc148Bn4ttr3GoYon2hVOp9wL7gEKcR69rt/kIGNnY9b3IFdHmciA3gd6v9cBPgfeAN4GLEyTXT4DdwEbgUaBzguSaCfzWfb2b+B0RNPn3C/iFu/4mICtRckW0+zswJ5FyEd8jgqb+bpXWmj4S037jEd6LL5xTEZuifN0A/A642W13C/BqrXU/BeSfZ9vnXd+rXBHtHga+n0Dv1yZ3GwJMBD7GvTXZ41w9gCDOtbBfAI96/X4BbXEKZkd3ejcNKAQt8fvltp0P/DSRcgH3Asti/d1qwVyraUAhaObf+dJa00diyhRr+GT6Ao5S/YyEAMdqLf8N8KPGru9VLrdNGnAAyE6g9+tl4MqI6Y+Iw6fJeLxfEW0HAJu8fr9wzu8W4xSA3UAFzifAngn2fvVPhPcros2dwLtA23hkiuf7RXyPCJr6/+I2oJf7uhewLZb9puo1giLgCvf1FGBHeIGIBIDZwKLGrO9xLoCrga2qWhCnTPHI9Zy7HiIyFEgnPj03NimXiPSKmLwR51NXPDQ6l6rmq2p3VR2gqgOAAmC8qn7iZS63zZCIyZnA1jhkikeuqcA9wExVPRWnTE3O1UyamukFnKKJ+/35mPYar+qaSF/AZUAesAHnMHxCxLIrgTVR1lmIW9XPt76XudzpvwJfT7D3Kx14HOcP7VpgSoLk+huQj3ON4AXcT0pe56o1fzfxu0bQ1PdriftvuBHnXHyfBMm1E+ec+Xr3K153MzU11404hbwM5yj9lQTI1BV4DaeAvAZ0iWW/1sWEMcb4XKqeGjLGGBMjKwTGGONzVgiMMcbnrBAYY4zPWSEwxhifs0JgjDE+Z4XAGGN87v8DnQZ4Gk57NPkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [3235,2385]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t]=1\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2672 14\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PREC_ID</th>\n",
       "      <th>ENR_DESC</th>\n",
       "      <th>COUNTY_NAM</th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SACDSHI</th>\n",
       "      <th>G20SACRGOR</th>\n",
       "      <th>G20SACDCUB</th>\n",
       "      <th>G20SACRDIL</th>\n",
       "      <th>G20SACDSTY</th>\n",
       "      <th>G20SACRCAR</th>\n",
       "      <th>G20SACDYOU</th>\n",
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       "      <td>01</td>\n",
       "      <td>PATTERSON</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2299</td>\n",
       "      <td>566</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>566</td>\n",
       "      <td>2276</td>\n",
       "      <td>571</td>\n",
       "      <td>2296</td>\n",
       "      <td>548</td>\n",
       "      <td>2280</td>\n",
       "      <td>567</td>\n",
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       "      <td>568</td>\n",
       "      <td>POLYGON ((1839239.963 762333.301, 1839240.297 ...</td>\n",
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       "      <td>02</td>\n",
       "      <td>COBLE</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2387</td>\n",
       "      <td>559</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>558</td>\n",
       "      <td>2347</td>\n",
       "      <td>562</td>\n",
       "      <td>2367</td>\n",
       "      <td>534</td>\n",
       "      <td>2349</td>\n",
       "      <td>555</td>\n",
       "      <td>2359</td>\n",
       "      <td>543</td>\n",
       "      <td>POLYGON ((1840088.847 807206.254, 1840090.437 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07</td>\n",
       "      <td>ALBRIGHT</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>581</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>578</td>\n",
       "      <td>1945</td>\n",
       "      <td>596</td>\n",
       "      <td>1977</td>\n",
       "      <td>570</td>\n",
       "      <td>1963</td>\n",
       "      <td>579</td>\n",
       "      <td>1954</td>\n",
       "      <td>585</td>\n",
       "      <td>POLYGON ((1871943.040 801230.531, 1871943.510 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H08</td>\n",
       "      <td>H08</td>\n",
       "      <td>GUILFORD</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>614</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>607</td>\n",
       "      <td>76</td>\n",
       "      <td>612</td>\n",
       "      <td>74</td>\n",
       "      <td>614</td>\n",
       "      <td>77</td>\n",
       "      <td>602</td>\n",
       "      <td>76</td>\n",
       "      <td>602</td>\n",
       "      <td>POLYGON ((1702354.926 805008.445, 1702627.359 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>079</td>\n",
       "      <td>079</td>\n",
       "      <td>MECKLENBURG</td>\n",
       "      <td>60</td>\n",
       "      <td>469</td>\n",
       "      <td>1223</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1212</td>\n",
       "      <td>452</td>\n",
       "      <td>1227</td>\n",
       "      <td>465</td>\n",
       "      <td>1209</td>\n",
       "      <td>454</td>\n",
       "      <td>1224</td>\n",
       "      <td>456</td>\n",
       "      <td>1220</td>\n",
       "      <td>POLYGON ((1410451.445 548338.307, 1410462.273 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
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      ],
      "text/plain": [
       "  PREC_ID   ENR_DESC   COUNTY_NAM  COUNTY_ID  G20PRERTRU  G20PREDBID  \\\n",
       "0      01  PATTERSON     ALAMANCE          1        2299         566   \n",
       "1      02      COBLE     ALAMANCE          1        2387         559   \n",
       "2      07   ALBRIGHT     ALAMANCE          1        1996         581   \n",
       "3     H08        H08     GUILFORD         41          73         614   \n",
       "4     079        079  MECKLENBURG         60         469        1223   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREOWRI  ...  G20SACDSHI  \\\n",
       "0          27           6           5           4  ...         566   \n",
       "1          15           4           4           3  ...         558   \n",
       "2          20           9           1           4  ...         578   \n",
       "3           3           1           2           2  ...         607   \n",
       "4           8           9           2           6  ...        1212   \n",
       "\n",
       "   G20SACRGOR  G20SACDCUB  G20SACRDIL  G20SACDSTY  G20SACRCAR  G20SACDYOU  \\\n",
       "0        2276         571        2296         548        2280         567   \n",
       "1        2347         562        2367         534        2349         555   \n",
       "2        1945         596        1977         570        1963         579   \n",
       "3          76         612          74         614          77         602   \n",
       "4         452        1227         465        1209         454        1224   \n",
       "\n",
       "   G20SACRGRI  G20SACDBRO                                           geometry  \n",
       "0        2274         568  POLYGON ((1839239.963 762333.301, 1839240.297 ...  \n",
       "1        2359         543  POLYGON ((1840088.847 807206.254, 1840090.437 ...  \n",
       "2        1954         585  POLYGON ((1871943.040 801230.531, 1871943.510 ...  \n",
       "3          76         602  POLYGON ((1702354.926 805008.445, 1702627.359 ...  \n",
       "4         456        1220  POLYGON ((1410451.445 548338.307, 1410462.273 ...  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/nc_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2347</td>\n",
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       "      <td>9</td>\n",
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       "      <td>4</td>\n",
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       "      <td>578</td>\n",
       "      <td>1945</td>\n",
       "      <td>596</td>\n",
       "      <td>1977</td>\n",
       "      <td>570</td>\n",
       "      <td>1963</td>\n",
       "      <td>579</td>\n",
       "      <td>1954</td>\n",
       "      <td>585</td>\n",
       "      <td>POLYGON ((-79.43265 35.95070, -79.43265 35.950...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H08</td>\n",
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       "      <td>602</td>\n",
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      ],
      "text/plain": [
       "  PREC_ID   ENR_DESC   COUNTY_NAM  COUNTY_ID  G20PRERTRU  G20PREDBID  \\\n",
       "0      01  PATTERSON     ALAMANCE          1        2299         566   \n",
       "1      02      COBLE     ALAMANCE          1        2387         559   \n",
       "2      07   ALBRIGHT     ALAMANCE          1        1996         581   \n",
       "3     H08        H08     GUILFORD         41          73         614   \n",
       "4     079        079  MECKLENBURG         60         469        1223   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREOWRI  ...  G20SACDSHI  \\\n",
       "0          27           6           5           4  ...         566   \n",
       "1          15           4           4           3  ...         558   \n",
       "2          20           9           1           4  ...         578   \n",
       "3           3           1           2           2  ...         607   \n",
       "4           8           9           2           6  ...        1212   \n",
       "\n",
       "   G20SACRGOR  G20SACDCUB  G20SACRDIL  G20SACDSTY  G20SACRCAR  G20SACDYOU  \\\n",
       "0        2276         571        2296         548        2280         567   \n",
       "1        2347         562        2367         534        2349         555   \n",
       "2        1945         596        1977         570        1963         579   \n",
       "3          76         612          74         614          77         602   \n",
       "4         452        1227         465        1209         454        1224   \n",
       "\n",
       "   G20SACRGRI  G20SACDBRO                                           geometry  \n",
       "0        2274         568  POLYGON ((-79.54243 35.84340, -79.54242 35.843...  \n",
       "1        2359         543  POLYGON ((-79.54039 35.96668, -79.54038 35.966...  \n",
       "2        1954         585  POLYGON ((-79.43265 35.95070, -79.43265 35.950...  \n",
       "3          76         602  POLYGON ((-80.00573 35.95770, -80.00481 35.958...  \n",
       "4         456        1220  POLYGON ((-80.97461 35.24055, -80.97459 35.241...  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2662 0.5068420065378582 5443067 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n"
     ]
    },
    {
     "data": {
      "image/png": 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/AfMjy8cDTuDUyDcA40RklIg4gRuBf/ZV8ANBWU0L8x56nen//Qr3/WM722vCX/WGOltO2u7+66bw8TnhKq/bLinkweun4dDh/5SKOSmuJGaNyOLNff3XzLI3VTdDgKci9fQ24DljzNJI4n5SRLYDPuAWY4wRkaGEm1EuNsYEROQuYDnh5pVPGmN2ROmzJBxjDDc/sY6K+jaSHTb+sPYQl9qSwAlySrPPwtwUHrx+KncvGEdOynm001dKRd28cbk8umIPNc0+svvh7/WMRT5jzDZjzExjzDRjzBRjzPciy33GmE9Els0yxrweWV5hjFncZf+XjDHjjTFjjDE/iN5HSTyt/mBHT5Mbv7UQgFqTGlnZfS94g9OTdSBvpWLcvHG5GANv7e+f6hvNCDHM40xi4pB0CnM8pLqSWPrFefzsU1eFV7ZY092pUur8TRuWQVpyEm/2Uz29JvoYN6Mgs6NUP2VYBqNHRO5t91CiV0rFviS7jYtG57B6bzUnN1SMDk30MW7FrnDrmsa2cKsbnKlgc2iJXqk4N29cLkfqWjl0ouXMG58nTfQx7ESTt2Mc1xRn5L65CHiy39sHj1IqrsyLtKFfvS/61Tea6GOYNxAC4Lvvn3Ryp2TubK26USrOjcpNYVimmzX9UE+viT6GLdseHqzbeepj0u4saNESvVLxTES4ZGwOb+2vJhiKbj29JvoY1eIL8Oire5g4JJ2Pzyk4eaVHS/RKJYJ54/JoaAuwrbwuqsfRRB+jXi+ppNEb4NtLJr63UzJ3lt6MVSoBXBIZm/mNPdF9SlZHmIoRy3cc4/sv7iQv1cX8okFsKavHbhMuHNXNIN3tJXpjYrpLZaXU6eWkuigemcXrJZXcvWB81I6jid5i9S1+Xtp+lG++8C4hA2U1rbxzuA6Az146Cnt3I0O5syHoA18zuFL7N2ClVJ8qLszmiTdLo9ptsSZ6C1U2tnHZj/5Nmz+E3SYs+9I8Rman8OVnt+Bx2fmva4q639ETHiqQ1hpN9ErFuenDM/AHDbuONjKjIDMqx9BEb6HnNpTR5g9x77VF3HThCDLcDgB+dfMFp9/RHUn0LTXhQVWUUnFrWiS5byuvi1qi15uxFnjncC1/WX+YP6w9xMVjcrjzijEdSb5XOkr02sRSqXg3NCOZ3FQXW8rqonYMLdH3s7KaFj72q7UEQgan3cadV4w5+zdxd6m6UUrFNRFh+vAMtpXXR+0Ymuj72b7KJgIhw/0fnMKSqUPIOpe+qN1Z4VdtYqlUQpg2PJPXd1fS5A2Q6ur7tKxVN/1s46Fwcr5ifN65JXnoTPRadaNUQhg/OBVj4GB1c1TeXxN9P6lq9LLhYA2PryplwcRBFGR7zv3NkpzgTNMSvVIJYmROCgAHT0Qn0WvVTT/YWlbHR371Fv5guD+Lhz487fzf1JOldfRKJYiROeGCX7S6LNYSfZSFQoZbf7e+I8nfenEhOamu839jd7aW6JVKECmuJDLcDiob2qLy/lqij7KHlpdQ2+LnnqvHc+cVY7t/0vVcaMdmSiWU7BQnJ5p9UXlvTfRRcqC6mWc2HObxVaUUj8zi81eMPblP+fPlzoaaA333fkopS2V5HNS2aKKPG8YY7vzjJkqONbJw0mB+duPMvk3yoKNMKZVgslNcHKlrjcp7ax19H6hp9hHqMnDAH98+RMmxRr5w5Rgev/kC3M4odFTkzoa2eggF+/69lVL9LtPjoF5L9LGhoc3PoeoW3tpfTYsvyKETzfzflgrmFGZz76IJZLgd/G7NQYpHZvH/Fk54b1/yfcWdBRhorYOUbroyVkrFnWjlC030Z+HxVft54KWSbtetP1jDh3+5tmP+s5eN7vvqmq669mCpiV6puOcPhkiya6K31KZDtTy0bDfJDhu3XjyKBRMHUTQkneMNbQzLdPOX9Yepb/WT6koiL83FB6YPjW5AXXuwVErFvUDQkBSlwuEZE72IJAOrAFdk++eNMd8Rke8CnwXax8D6hjHmpW72Pwg0AkEgYIwp7pvQ+0cwZCitauLLz24hN9XJK3dfToans6fJ1Lxwf/C3XTKqfwPztHeDoIleqURwotl7dr3YnoXelOi9wHxjTJOIOIA3ReTlyLpHjTE/7sV7XGmMqT7nKC1y6EQz7/vZmzR5AyQ7bDxxy+yTkryltESvVEI5fKKFuaOjUw17xkRvjDFAU2TWEfkxPe+ROJ7fVE6TN8B/XjWOG2YXMCzTbXVInTzaVbFSiaLNH+RoQ1tHnzd9rVfNK0XELiJbgErgVWPMusiqu0Rkm4g8KSJZPexugFdEZJOI3H6aY9wuIhtFZGNVVXRHRO+NUMjwwuYjXDoul68sHB9bSR7AlQ62JC3RK5UATjT7MAbyM/qge5Ru9CrRG2OCxpgZwHBgjohMAX4JjAFmAEeBR3rY/RJjzCxgEfAFEbmsh2M8bowpNsYU5+Xlnd2n6GO1zT7u+OMmymtbuX7WMEtj6ZFIuImlPjSlVNxragsAkJYcnarhs3pgyhhTB6wErjXGHI9cAELAb4A5PexTEXmtBF7oabtY0eYPMvP+V3ll53GunzWMxVOHWB1Sz9za341SiaDJ6weIyqAj0ItELyJ5IpIZmXYDC4ASEemaAT8EbO9m3xQRSWufBq7ubrtYUvTtZQBMHZbB/3xsBq6kKDzV2lfcWVp1o1QCaIyU6FOilOh7865DgKdExE74wvCcMWapiDwtIjMI18EfBD4HICJDgd8aYxYDg4EXIk97JQF/NsYs6/NP0Ue6dhH6989fbGEkveTJhrrDVkehlDpPTd72qhuLEr0xZhsws5vlN/ewfQWwODJdCkw/zxj7zc9f3wfAsrsvxWGPg26A3NlQscXqKJRS56k5kugtq7oZSP6x5QgARfnpFkfSSzrKlFIJob3qJjVKJXpN9F1cODqHsYNSrQ6j99zZEGgDX3SGH1NK9Y/2qpsUpyb6qBuc7oraUF5RoQ9NKZUQGloDeJz2vhuB7hSa6COavAHWH6ghPyPZ6lB6T7tBUCohlNe2RPWhTE30ES9sPsKe403cftkYq0PpvY4SvT40pVQ8K6ttZUS2J2rvr4k+4khteAivC0dlWxzJWXBr1Y1SieBIbQvDsrREH3XDMsNVNlEdLKSvuSPdC2nVjVJxq7HNT0NbQKtu+kNhbrjXuDV746g3Zb0Zq1Tcax8QXEv0/cAfDAEQMnHUA3OSCxwp0KJ19ErFq/ZqYy3R96G3S0/wt03lJy1r8gZ47N/7yUtzcf2s4RZFdo482rGZUvGsoj7cpHtoFBP9gBoz9tCJZm58/G0g/DVpTmE2f3unnB8t3011k5dHPjodZ1KcXfu0YzOl4lptsw+A7BRn1I4xYBJ9MGS4569bcSXZ8AZCHQm/3U9vnMF1M2K07/nT0RK9UnGtptlHmispqv1rxVnx9dx96ZnNbDhYy/3XTWHRlHyAju4OfvChKXxg+lArwzt37mwt0SsVx/ZXNTEyN3pt6GEAleiXbjuKx2nnY7ML+NjsAqvD6Tue7DM+MPXGnir+sfkID31kWnz0yqnUAGGMYfuReq6elB/V4wyIRO8NBAG48/I4euq1t9zZ0FYHoRDY3pvEKxvbuOXJ9QCMyPFww+wCHl62m0DIkJ+RzKA0FzmpTrI8TnJSXGSlOMhOceJ22ImMI6CUipKK+jZqW/xMGRbdHnMHRKKvbw0P05UZxZsdlnFngQmFk73n5Kd6m7wBfrlyf8f8T1bs5Scr9gKQ5XHQ7AviC4S6fVtnko1Mt4MMt4NMT/g1w+08aT7T4yDL4yQ7pfMn2RHDI3IpZbHKxjb+vO4wBVkePnzBcFbvqQJg6vDMqB53QCT6hkiiz3BHZ+BdS3Xt78aTTasvSE2Ljx8tK+EfWyoAGJWbQpbHwTuH6wB46lNzuHx8HsYYGloD1LT4qGn2UtPsp7bZR02Lj9pmH/Wtfupa/NS3+qmoa2PX0UbqW/0dXap2J8VpJyvFSU5H8neRneIgO8XVsax9fU6qk1RXkn5zUAnPGMMf1x3mgRd30eoP1zCsP1DD0m0VTBuewbRhGVE9/oBI9HUtkRJ9Iib6Lj1YhrJGM/G+947UeP3MYdx5RbjaKqlLHb2IkOFxkOFxMCryZHBv+IMhGlr91LVGLgyRnxNdpmuafVQ3+dhzvIkTzV7a/N1/c/A47QxOT2Zwuov89GQGZySHX9OTyUlxMmdUtl4IVFyrbGjjq3/bxsrdVVw6LpdvvW8SP3t9L89uLGPa8Awev7k46l2vDIhE3151k56IiT5Sot+x/yDve6yyY/H0gkx+esMMAiHT54OpOOw2clJd5KS6IK93+7T4AidfEJp8nGj2crzBy7GGNiob2th0uJbj9V58wc6LQtw2e1UDXihk+MPag/zktb20+YN877rJ3Dx3JCLCYzfN4tGPhXDYpV8KMgMi0Te0JWbVTZs/yPeWH+EB4IlXNgKXMSzTzRv/dcVJJfdY4HEm4XEmMTzr9M3IjDHUtvj5wp/eYW3pCeaNze2nCJXqO1WNXr7y3BZW762meGQWD31kGmPyTi5w9efDmbGVDaKkoTVcp5wepfEYrfLEmwdYujf8+HSWNLHynitY87X5MZfkz4aIkJ3i5IvzxwLhpqEQ7uGvJvIEoVKxbGtZHUt+vpr1B2p48Pqp/PWOi96T5PtbYmW+HrTfjE1LTpwS/ZG6Vh5evhvBQwgb356fD2dRzx7r/KFw53LVTV5qmn3MffA1Mt0O1n79qqgNt6bU+Vqx8zhf+PM75KW5eOHzlzBpaHSbTfbWwEj0bX7cDnv89WNzihZfgD+sPcS28jpeevcYAAYbNk9Wwo0y9fTaQwzNSOajFxRw55824QuEqGz0smLXca6ZHN2HS5Q6F0u3VXD3M1uYPDSdJ2+dHb6HFSPiO/P1UkNrgHR3/F7T/MEQf1p3iMsfXskPXy7pSPILJg6i9IHF4ZY3CdbfTVWTlzGDUnl0xR7eLq3h4Y9MY1imm9+vOWh1aEq9xwuby/nPv2xm5ohM/viZC2MqycMAKtGnx2G1jTGGl7cf4+HluzlQ3UzxyCyuKhrEMxvKGJbp7myWlYA9WAaCIVbvrWb13mpuv2w0Hy0u4ESzjx++XELJsQaK8mPjK7FSu442cO/z73LhqByeuLUYjzP20mrsRRQFDW3+uGta+db+ah5atputZXWMG5TKbz9ZzFUTBwHwpQXjyE9P7myW5cmGhiMWRtv3rioaxI6KBi4ancO91xYBcENxAY++uoen3jrIg9dPszhCpcLdq3z52S2kux384qaZMZnkoReJXkSSgVWAK7L988aY74jId4HPAlWRTb9hjHmpm/2vBX4K2IHfGmN+2Eex91pDa4Dc1Pjo/mBHRT0/WrabN/ZUMTQjmYc/Mo3rZw0/6QbkkIxTBihwZ8Ox7f0caXR99rLRuJ1J3HThiI7PnpXi5EMzh/HC5iN89ZoishKxSwsVV36yYi8lxxr57SeLY666pqveXH68wHxjTJOIOIA3ReTlyLpHjTE/7mlHEbEDjwELgXJgg4j80xiz83wDPxsNbX5G58V2i5SymhYeeWU3/7elggy3g28unsjNF43sXd8xCdgnfVqyo+Np3q5uubiQZzaU8ezGMu5IxE7qVNx4a381v35jPzcUF7Bg0mCrwzmtMyZ6Y4wBmiKzjshPbwdWnQPsM8aUAojIM8B1QP8m+tbo1dGHQobDNS00eQO0+oO0+IK0+gK0+NqngzT7ArRG5lt8QVr9netbItuW1bRgtwl3XjGGOy4fc3YPd7mzwN8C/jZwJEflc8aKiUPSuXBUNk+vPcRnLx2tTS2VJR58aRe/XlVKQbabb79/ktXhnFGvKpQiJfNNwFjgMWPMOhFZBNwlIp8ENgL/zxhzahu/YUBZl/ly4MIejnE7cDvAiBEjzupDnEmzL8if1h3i2Y1lCGATQQQkfFwEIDJvs8lJyyWysr06vH3scBFw2KRjvMczcSXZ8DjteJxJuJ12Upx23E47g9KScTvtLJg4mE9dMor8jHNI1B0dm9WAI04HUDkLt1xcyOf/9A7/LqmM+ZKUSjxv7avm16tKuX7mML62qIhUV2zWy3fVqwiNMUFghohkAi+IyBTgl8D9hEv39wOPAJ86ZdfuilvdfhswxjwOPA5QXFzc228MvfLAh6ayr7IJgyHyD2MMIRNO3AbTkcA7lkeWhbdtDzv8cUTC2wWChkDI4HbauWxcXiSR2yOP+4en3ZH5qJY8u3RsRnriJ/qFkwaTn57MU2sPaqJX/arFF+Dev29jVG4KD1w/NW665T6rS5Expk5EVgLXdq2bF5HfAEu72aUc6Dqc03Cg4hziPC8fuWB4fx+yf3XtqngAcNht/MeFI3jk1T3sr2qy/PFyNXA88soeympaefb2uXGT5KEXD0yJSF6kJI+IuIEFQImIDOmy2YeA7pp9bADGicgoEXECNwL/PO+o1cncXapuBogb54zAYReeXnvI6lDUAPHO4VqeXHOAT8wdwYWjc6wO56z0pkQ/BHgqUk9vA54zxiwVkadFZAbhOo2DwOcARGQo4WaUi40xARG5C1hOuHnlk8aYHVH4HAObOyv8mmAPTZ1OXpqLuaNzeLv0hNWhqAHAGwhy7/PbyE9P7niuI570ptXNNmBmN8tv7mH7CmBxl/mXgPe0r1d9yDPwSvQQvnficcbP12cVv379Ril7K5v43a2z47JzxAHR103Cc7ghyT2gSvQAjd4AqXH4R6fiS1lNC4/9ex+Lp+ZzZdEgq8M5J5roE4Une8DcjG3X1OYnLcHGGFCx5wcv7sImwjffF/vt5XuiiT5RuLMHXom+LUBaHLRhVvFr9d4qlu04xl3zxzIs033mHWKUJvpE4ckacHX0Td5AXDysouKTLxDiu//cQWGOh89cOsrqcM6LJvpEMcBK9MGQocUXjMsbYyo+PLnmAPurmrnv/ZNwJcX3TX8tDiWKAVZH39QWHge4tLqJ7UfqSbILSTYbTrsNR5LgtNtwOewkJ9niegxdZY0jda38dMVeFk4azPyi+H/6WhN9onBHEr0xdHTMk8BCkT4r/rGlgn9sOf3D1kk2wZVkI9lhJ9lhx5UUuQg4bJ3Lk+y4HDaSkyLLIxcJV2R7Z5INh91Gkk1w2CPTdsFhl8hyW8f0qLyUuBzoRnX6/tKdGAzfiYMOy3ojsRK9vwXe+jnsWW51JP2v5QSYIHgbIDnD6miiLivFydIvzqOysY1A0BAMGXzBEIFg+NUXCOENBGnzh2jzB/EGwq9t/s7l3kAQrz9ETbOvx23ORX56Mq985TJN9nFq8+FaXt5+jC8vGM/wLI/V4fSJxEr07QZPtjoCa6QNBdfAGWJvyrAMIHoXNWMM3kAIrz+EPxTCH7mQ+IMh/JHXQKh9PrzuWEMb9/5tGz9bsZdvLUmM0uBA8+NXdpOT4uTTcX4DtqvESvTfrbc6ApVARKSjuudsbD5cy+/fOsgNswsYNzgtStGpaHhrfzVr9p3g20smJVSLLr1LpVQfu+fqCXicdr77rx0Y06c9bqso++XK/eSlufiPC/t2TAyraaJXqo/lpLq455oJrNl3gpe3H7M6HNVLOyrqWb23mtsuKYyrLoh7QxO9UlFw05wRFOWn8YMXd9HqC1odjuqFx1eVkuK08x8XjrQ6lD6niV6pKEiy2/jedVM4UtfK+3622upw1BmU17awdNtRbrpwxNmN1xwnNNErFSVzRoW7jy6tbqbkWIPF0ajT+d2agwhw2yWJ09KmK030SkXR0i/OA+DHy/dYHInqSZs/yPObyrl2Sj5D47jjstPRRK9UFE0ZlsG91xaxYtdxVu6utDoc1Y2Xtx+lvtXPTQnW0qYrTfRKRdmn5hUyKjeF7/1rJ77AuT1tq3qvzR+ktKqJ3ccaCQTPfL7/sq6MwhwPF8XZOLBnI3GeCFAqRrmS7Nz3/knc9rsNPLnmAHdcPsbqkBKKNxBk7f4TvLarkpV7Kimrae1Yl+yw8cX547jz8jHYbO/tA2pfZSPrD9bw9UVFSAL3EaWJXql+cOWEQSyYOIifv7aXD80cxuD0ZKtDims1zT5eL6lkxc7jrN5bRbMviNthZ964XD4yq4DhWW5sNnj53WM8vHw3/9pawb2LirhywslDAf79nSMk2YQPXzDcok/SPzTRK9VPvr1kEgsfXcWDL+3iJzfOtDqcuNTQ5ucXr+/j92sO4guGGJzu4oMzh7Fg4mAuGpPzngedPjhjGP/cWsEjr+zhtt9t4F93zWPq8M7+kd7cV83MEZnkprr6+6P0K030SvWTkTkpfO6y0fz89X3cdOHIjuaXqnde2FzO/Ut3Udvi48OzhnPrxYVMHpp+2ioXEeG6GcOYNCSdhY+uYn9VU0eir2/x8+6Rev5z/rj++giW0ZuxSvWjz18xliEZydy/dCehkPaD0xt7jzfywcfW8OVntzI6N4V/3TWPH390OlOGZfS6Xn1Ejof05CRW7anqWLa29ATGwCVjc6MVeszQRK9UP3I77dx7bRHvHqnn75uPWB1OzHvncC0f+dVaymtb+db7JvLnz86NdE99dlxJduYXDWL1vuqOjubeOVyL025jekHij9+giV6pfvaB6UOZXpDJw8tLaPEFrA4nZq3aU8V//GYdmR4HL3z+Yj5z6WicSeeesi4ozKaq0UtpdTMQ7k560tD0uB8Ptjc00SvVz2w24b4lEzne4OVXb5RaHU5M2lnRwO1Pb6QwN4Xn77iYguzzH+lp4cTBOO02frSshKffPsSGg7VcVTTozDsmgDMmehFJFpH1IrJVRHaIyH+fsv4eETEi0m1Fl4gcFJF3RWSLiGzsq8CVimcXjMxmybQhPL5qP0frW8+8wwBS3+rnjj9uIsPt4KlPzSYvrW9axORnJPPlheNZvuM43/6/7cwZlc1nLh3dJ+8d63rT6sYLzDfGNImIA3hTRF42xrwtIgXAQuDwGd7jSmNM9fkGq1Qi+dqiIl7ZeZwfLdvNozfMsDqcmGCM4b/+upWKulaeuX0ug9L69nmDO68Yw7yxuVQ3eZk3LheHfWBUapzxU5qwpsisI/LT3lzgUeCrXeaVUr00PMvDZ+aN4oXNR9hSVmd1ODHht6sP8MrO43xtURHFhdFpfjp1eAZXFg0aMEkeellHLyJ2EdkCVAKvGmPWicgHgCPGmK1n2N0Ar4jIJhG5/TTHuF1ENorIxqqqqp42UyqhfP7KseSmuvj+0p0DftjBZduP8cNlJVwzeTCfnpeY3QVbpVeJ3hgTNMbMAIYDc0RkGvBN4L5e7H6JMWYWsAj4gohc1sMxHjfGFBtjivPy8noXvVJxLtWVxD1Xj2fjoVpeendgDjtojOGRV3Zzxx83MXloOg9/dHpC9ztjhbP67mKMqQNWAtcBo4CtInKQ8AXgHRHJ72afishrJfACMOe8IlYqwXy0uICi/DQefHkXbf6BNeygMYb7l+7i56/v44biAv56x0WkJyfeCE9W602rmzwRyYxMu4EFwGZjzCBjTKExphAoB2YZY46dsm+KiKS1TwNXA9v79iMoFd/sNuHbSyZRXtvK7986aHU4/SYUMnztb+/y5JoD3HZJIT/88NQB0abdCr0p0Q8B/i0i24ANhOvol/a0sYgMFZGXIrODCbfS2QqsB140xiw736CVSjSXjM1lwcRB/OL1fVQ3ea0Op1/8cFkJz24s464rx3LfkklaXRNFEos3gIqLi83GjdrkXg0s+6uauObRVXxsdgEPfGiq1eFE1R/WHuS+f+zg5rkj+d51kzXJ9wER2WSMKe5u3cBpX6RUjBuTl8on5o7kmfWH2X2s0epwombFzuN89587WDBxEN95v5bk+4MmeqViyN0LxpGW7OD7LyZmc8udFQ188S+bmTIsg599fCZJA6gtu5X0LCsVQzI9Tv7zqnGs3lvNyt2J9TxJIBji3r9tI8WVxBO3zMbj1OEw+osmeqVizM1zRzIqN4Xvv7gTfy8Gt44XT609xLtH6vnO+yf1Wf81qnc00SsVY5xJNr6+qIj9Vc38Zf2ZupGKD0fqWnnkld1cMSGPJdOGWB3OgKOJXqkYtHDSYC4ancOjr+6hvtVvdTjnxRjDd/6xHWPg/uum6M1XC2iiVyoGiQjfWjKRulY/v3h9r9XhnJfXdlWyYlclX144rk/6lVdnTxO9UjFq8tAMPnrBcH7/1kEORkZFijfVTV7ueX4r4wenctsl2lGZVTTRKxXD7rl6Ag67jR++XGJ1KOfkd2sOUN/q57GbZg2oboFjjZ55pWLYoPRkPn/FGJbtOMbbpSesDuesNHkDPL32ENdOzmfc4DSrwxnQNNErFeM+c+lohmYk8/0XdxIKxc9DVD9/bS8NbQHuvGKM1aEMeJrolYpxyQ479y4qYvuRBv6++YjV4fTKutIT/HpVKR+fU8C04ZlWhzPgaaJXKg68f9pQphdk8vDyElp8AavDOaOn1h4kO8XJfUsmWx2KQhO9UnHBZhPuWzKR4w1efv1GqdXhnFZFXSsrdlbygelDcTu1f/lYoIleqThxwchslkwbwq9X7edofavV4fTorf0n8AVD3DinwOpQVIQmeqXiyL3XFhEy8PCy3VaH0qNVe6rI9DgYm5dqdSgqQhO9UnGkINvDp+eN4u+bj7CtvM7qcN6jsc3Psh3HuG76UO2COIbo/4RScebzV4whN9XJ/Utjr8/6jQdr8QVCXDVxsNWhqC400SsVZ9KSHXxl4QQ2HKzl5e3HrA6nQyhkeGhZCUMzkpldmG11OKoLTfRKxaEbZhdQlJ/Ggy/vos0ftDocADaX1VJyrJEvLxyvrW1ijCZ6peKQ3SZ8632TKKtp5fdvHbQ6HADe2F2F3SZcMyXf6lDUKTTRKxWn5o3L5aqiQfzi9X1UN3mtDoct5fWMG5RKerLD6lDUKTTRKxXHvvG+ibT5g/zPq3ssjcMYw7byOqZrdwcxSRO9UnFsTF4qn5g7kmfWH6bkWINlcRyuaaGuxc/0gkzLYlA900SvVJy7e8E40pIdfH/pLsuaW24trwdg2vAMS46vTk8TvVJxLtPj5O4F43hzXzWvl1RaEsO2sjpcSTYm5Gu/87HojIleRJJFZL2IbBWRHSLy36esv0dEjIjk9rD/tSKyW0T2icjX+ipwpVSnT8wdyei8FH7w0i78wVC/H39reR2Th6brKFIxqjf/K15gvjFmOjADuFZE5gKISAGwEDjc3Y4iYgceAxYBk4CPi8ikPohbKdWFw27jm4snUlrVzB/fPtSvxw4EQ2w/0qD9zsewMyZ6E9YUmXVEftorAh8Fvtpl/lRzgH3GmFJjjA94Brju/EJWSnVnftEg5o3N5Scr9lLX4uu34+6raqLVH2R6gdbPx6pefc8SEbuIbAEqgVeNMetE5APAEWPM1tPsOgwo6zJfHlnW3TFuF5GNIrKxqqqqd9ErpTqICN9aMpHGNj8/fW1vvx13W1n7jdjMfjumOju9SvTGmKAxZgYwHJgjItOAbwL3nWFX6e7tejjG48aYYmNMcV5eXm/CUkqdoig/nRtmj+DptYfYX9V05h36wJbyOtKSkxiVk9Ivx1Nn76zunBhj6oCVhKtfRgFbReQg4QvAOyJy6rPP5UDX0QeGAxXnGKtSqhe+snA8yQ47D760q1+Ot628jmnDM7DZuivXqVjQm1Y3eSKSGZl2AwuAzcaYQcaYQmNMIeGEPssYc2pXehuAcSIySkScwI3AP/vyAyilTpaX5uILV45lxa5K1uyrjvrxSquamTA4PerHUeeuNyX6IcC/RWQb4cT9qjFmaU8bi8hQEXkJwBgTAO4ClgO7gOeMMTvOP2yl1Oncdkkhw7Pc3L90J8FQ9B6iqm7y0uILMijdFbVjqPOXdKYNjDHbgJln2Kawy3QFsLjL/EvAS+ceolLqbCU77Hx90US+8Od3eG5jGR+fMyIqx1l/oAZA+5+Pcfp0g1IJavHUfGYXZvHIK7tpbPNH5RjrSk/gdti164MYp4leqQQlEu6zvrrJx29WlUblGJvL6pg5IlOfiI1xZ6y6UUrFr+kFmSyZNoTfrD7AJ+aOZFB6crfb1TT7WL23iv1VzfgCIXyBEA67kJfmYsm0oeRnvHe/UMiw93hT1KqFVN/RRK9UgvuvayawfMcxHl2xlwevn9qxvLbZxx/WHuL1kuNsO1JPe8eXTrsNZ5INfzCENxDigZd2Mb9oMF9bNIGxgzo7Lauob6XVH2Tc4NT+/kjqLGmiVyrBjcxJ4T8uHMnTbx/i0/NGUZjj4ck1B/jZa/to9gWYWZDJ3VeN5/IJeUwdloG9S3v4QyeaeXZDGU+/fYhvvLCd5z53Ucc6m2i7+XihiV6pAeCL88fy/KZyPvnEOrJSnOyoaOCqokHcu6iI8YN77lp4ZE4KX722iLRkBw8tK2FrWV3H4CKeyADgrb7YGJxc9UzvoCg1AOSkuvjcZaOpqG+joq6VX31iFr+9pfi0Sb6rmy4cwZCMZO744yaO1rcCdJT8o9lOX/UNLdErNUDcfvlo8jOSuXpyPhnusxvAO8Pt4MlbZ7Pop6v5y/oyvrJwfEdLG58F/d+rs6OJXqkBwpVk56PFBWfesAcTh6STl+biaF24RF9e2wJAToqzT+JT0aNVN0qpXpsyNJ01+6pp8gZ46q1DJNmEK4sGWR2WOgNN9EqpXvv0vNEcb/Qy94HXePrtQ9w4p4DBPbTNV7FDq26UUr02b1wuT902h7+/U07RkDRuu2SU1SGpXtBEr5Q6K/PG5TJvXK7VYaizoFU3SimV4DTRK6VUgtNEr5RSCU4TvVJKJThN9EopleA00SulVILTRK+UUglOE71SSiU4MSb2uhgVkSrgkIUh5ALVFh4/Vuh56KTnopOei06xdC5GGmPyulsRk4neaiKy0RhTbHUcVtPz0EnPRSc9F53i5Vxo1Y1SSiU4TfRKKZXgNNF373GrA4gReh466bnopOeiU1ycC62jV0qpBKcleqWUSnCa6JVSKsFpoo8QkRki8raIbBGRjSIy55T1I0SkSUTusSrG/tLTuRCRhSKySUTejbzOtzrWaDvd74WIfF1E9onIbhG5xso4+4OIPBs5D1tE5KCIbIksd4jIU5Hfi10i8nWLQ426ns5FZN00EVkrIjsi58T6sRaNMfoTvk/xCrAoMr0YWHnK+r8BfwXusTpWq84FMBMYGpmeAhyxOlYLz8UkYCvgAkYB+wG71fH243l5BLgvMn0T8Exk2gMcBAqtjtGic5EEbAOmR+ZzYuH3QocS7GSA9Mh0BlDRvkJEPgiUAs39H5Yluj0XxpjNXbbZASSLiMsY4+3n+PpTT78X1xFObl7ggIjsA+YAa/s/xP4lIgJ8DGj/RmeAFBFJAtyAD2iwKLx+1c25uBrYZozZCmCMOWFVbF1pou90N7BcRH5MuErrYgARSQHuBRYCCV9tE3E33ZyLU3wY2JzgSR56PhfDgLe7bFceWTYQXAocN8bsjcw/T/jCd5Rwif7Lxpgaq4LrZ6eei/GAEZHlQB7hwsCPLIsuYkAlehFZAeR3s+qbwFWEf0H/JiIfA54AFgD/DTxqjGkKX7wTwzmei/Z9JwMPES69xL1zPBfd/TLEfVvl050LY8w/ItMfB/7SZd0cIAgMBbKA1SKywhhTGtVgo+wcz0USMA+YDbQAr4nIJmPMa1EN9gy0HX2EiNQDmcYYE/k6Vm+MSReR1UBBZLNMIES4Pu4XFoUadT2di8i64cDrwG3GmDVWxtkfTvN78XUAY8yDke2WA981xiR01U2keuYIcIExpjyy7DHgbWPM05H5J4FlxpjnrIs0+no4FzcC1xpjbo3MfxtoM8Y8bFmgaKubriqAyyPT84G9AMaYS40xhcaYQuAnwAOJnOQjuj0XIpIJvAh8fSAk+YhuzwXwT+BGEXGJyChgHLDegvj62wKgpD2xRRwG5ktYCjAXKLEkuv7V3blYDkwTEU/kQnA5sNOS6LoYUFU3Z/BZ4KeR/5w24HaL47FST+fiLmAs8O1ISQXgamNMpQUx9pduz4UxZoeIPEf4jzgAfMEYE7QuzH5zIydXVQA8BvwO2E64Sut3xpht/R2YBd5zLowxtSLyP8AGwlV5LxljXrQiuK606kYppRKcVt0opVSC00SvlFIJThO9UkolOE30SimV4DTRK6VUgtNEr5RSCU4TvVJKJbj/D3zJ4au7UjQ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic NC map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-84.5,34.8)\n",
    "pt2 = Point(-82.6,34.8)\n",
    "pt3 = Point(-82.8,36.1)\n",
    "pt4 = Point(-84.5,36)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #in case we want to clip southern tip\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  80 tracts w pop 251761.0  to reassign to tracts...\n",
      "[254, 702, 703, 861, 867, 932, 933, 935, 936, 939, 962, 1109, 2098, 2100, 2101, 2102, 2118, 2119, 2125]\n",
      "I have flagged  113 precincts to reassign to precincts...\n",
      "[512, 528, 562, 603, 661, 662, 669, 670, 672, 962, 1013, 1353, 1416, 1418, 1633, 1640, 1680, 1681, 2063, 2650]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tvraU9L+yDTaLA3cfPRmHS9j+ezL7VmcAsO23E/QaH0WX4aFkJBazZcFxygtNjL69A237BBI2IIBlb8SjOVTB0RMlxMV4t8rcfWtOUCq446tqQPr+DoLSubfKNS8GGuN1cxDoXsfxOiMMZFnOAcaf8fpl4OUWzFFBQeECY7fbyc/PJyEhgR3bd4EgofI1EB3Wl/qc8Izup33fIzr5ENrei/ICE6V51exbncHm+cc4sC6TikIT7n4Gxt/XhYiOPs72oe4Yh/hTs6mA1StSiHuwFYRelokwHSbZezB+9SUwy97rTG3QYZLTi+Y/ghIZq6CgAIDVakWWZex2O5WVlZSUlJCSkoLVauX48eOYTCYEQSAiOAZriYYSKYudCRvYn7CXx599AK2u4aAmlUrEO8gF7yAXorr4snt5Gnkp5bTtHUCvcZG17PdBga4kU4C7t75V7q/swFI8BRN2vU/dDSSHM9LV4AVX/K9VrnmxoAi9goICe/fuZfny5TgcjrOOa7Va1Go1arWa8ePHExsbi5eX0+XR4ZBYNm89+45vZsZnc7jn0amIjUw0JqpE+k5s2Efe66TAV1a0TgEP87ENAPTKnUtFyau4e5/OW4+1Gubf4kxJfOVXYPBslWteLChCr6Dw/5yCggKWLl1KREQEsbGxSJKEl5cXBoOB8PBwNJq60wOoVCITbxxB+dclpOQnsuDH5Vx3xxXYbHY2r95F2w7R6Aw6CnOLiesajVifuaQO7HaJTd8lYkRgQL/gVrnPwGs/YtsMgQFZM0hd9QVdb3jt9Mltn0PyX3D5x86gp/8YitArKPw/Jzk5GVmWGTduHAEBAefucAaCIHDz9Cl8/l4ZRzL2sHNzGHt27KegOo1Nu3Ga7wXoerg/k28a0+hxUzLLMToE7IJM187+TbuhBuhz23ukvLUG1xNLwfESJP3pTEoW/70zmrXXtEaPlWm2sqW0kgKLnQyzhTSTlSmBXtwYVI9p6AKiCL2Cwv9jrFYr27Ztw9/fH19f32aNIYoiN0+7nq8//5YVf/0OgKc2iDJLPka1OzWOMsoryps0pp+vEQkZS7AB9RkZK202B5u2ZtGtmz8+no2MVj0DtVpFZsAohuZ+D6+fIcghPWH0640ep9Rm58q9x8m22M46vreihhsCvRFaq/5sK6EIvYLC/1MOHDjAkiVLkCSJyy+/HJWq+RGg3v5u3Hr7zaxatoGomAiGj+uHxWJl9/pE1u5YQrv2TfNgyc2rQgBcss1k5VSyYOFROFKBVnYKaML8E/S5LY4BfUOaPNeQflNg8fcASBO/QOw8pcEUB7vKqthTUYNJksi12FhVVE6B1Y4AfN0hgiCdhlgXPWPij5FpvngKgp+JIvQKCv8PsdvtrFq1Cj8/P0aPHk1MTEyLxwyJCmDag9edeq3TaUlPzQRg1454khJTuGX6VajqSyR2Bh1ifdjY2wdpdwlLXtuNFijxVOMSYECnVaFJKGfTqrRmCX1M14EsMu3i4yXbeYxhXNWAyBdZ7dx0MIXKkwFeIjDOz4MgnYZh3u6M8jntaz/Iy5W5uSXkW+0E6upJe3yBUIReQeH/Ifv27aOmpoYpU6bQps358xfvPagLuUsyKDXnUmrOJT2lL9FtwxvV955pXfnctBdzqYWeQ0IZMfR0v/de3oIux0xScgnt2jTdx97g4k6W7IfdUTsGIMds5fZDqaSbrGhFAbMks7BbG3q5u+BAxqWOJ598i40VhU7zlLfm4suNowi9gsJ/iLKyMqqqqnB1dUWlUiGKIi4uLlitViRJoqamho0bN3Lo0CHCw8OJjm79NMBn0rZzJE90foBtf+1j9eYl5GUWNVroBUHgwQd61nlu0tSO/PnBXhZ9l8Bz7wxt8rxWJDsTJ8ohRr7LdP7fV6sm1WRhWUEZSTVmhni5YZVkHo0MYJCXW4PjfZKeT5nd0WCbC4ki9AoK/xEKCgr48ccfqampOeu4m5sb1dXVSJLT/KDRaOjevTtDhgw5r5uGkkPi649/orL69EastpVMGu3aeLEw1IBnppncvCqCAptWrq9McK7kH9mbiuxyWgYFoKOrgf/FhTMlsOEnhZQaZ/WpaKOOI1UmACL0WrRNcCP9t1CEXkHhEkeWZVasWMGuXbvQarUMGzYMd3d3JEnCYrGQlpaGn58fRqMRs9lMz549TwU9nU+KC8soqEoHQCXr6BDRi16DOrfa+AGhrlgyzaRkVTRZ6E/kVSKrBJYMaEcbVwOCAIVWO15qFf6N+DLKtVgZsNOZn3FJ9xiijTp2lFfzVFTrFS9vTRShV1C4xDl69Ci7du0iOjqaKVOm4OLictb5gQMHXpB5+fh7osUFq1zDyMFjGDCqR6uOX3i0DHfAy7VpNVkdkkRuViWCQyZKr8VH65RBb8255fD3/FJWF1fwW37pqWOT9p0AYKS3+zmfAi4UF98zhoKCQpP4e3UeFhZWS+QvJGaTlRtvugGtYGT9lr9qpSZuKd1HhQGweEYC1TW2c7Q+jUoU6Rjn9KFfl1HS6H6/55dyT2LyWSL/Z49Y3msbys+do5jZObLRY/3bKCt6BYVLGKvVytdffw2At/fFs5pcs2QLW/eudRq9T5KbVURIROtFuY4dGcU3yeV47i1h9uxE7r6rW71tLRYbkt2OwcVAldXOocNFCMCVMY2bT25pOW+sfBY/tqJTGXhv5I8M8I9DrxLp4XHxfLnWhyL0CgqXMGfmoenatesFnMnZHD9+DASIC+tOTn42Xu4+BIU1L/K2Ie66swvvP7AeIa2y3jZrP56J249forHb+HzgtawP6oHgkNG5abBJMmcafixmM5npmSStWI0lJQVtZQVUVpLFQSxXON0mLQ4TD6++jp/H/Ux3/1oZ3C9KFKFXULiEEQSBqZ1FtAmzyX1jEYIogqjF6hkNbgEI3tG4RPfBu92Af21ONpMJ931/4WfV0j28G5GPTkNtaJ1Uw//EIcloJBDd606RfGhTPCHfvA9AtrsfD2z7FQZC3M1X89iAWNRnZNtMT00n99pr8aisIBqwqdXYVSosGpEPp4pE58H8x3ezNmMtz25+lq3ZWxWhV1BQOM8UHIH9s4k69AXVOj/MDhsO0QW1rRzvvL/Q5p20W++EvIGvEzj6oX9lWmkbN9Jzz14AbAk7OPjdl8TO+xm3iNb3SFGJArJKoKzUfNbx3b+vIW/WXKIO7cCk0iJ+8BkhYb4U33knT26cRc2OhSzr2x+xcxe8AwMI7dKJQ8++QFRVJYk330bc2NF07tkdSZb5ftZjFMl/8WL0PejVeoaFDgPAqDG2+v2cLxShV1C4FClMgq8HAzJC5GBcp8zA1fW0vVlyOKiuKqM6fT8suR+vra9j6jIeQ0DLUx2ci/ChQ0kBrFot1sAOuGbsp3BjPG5TL2/1a5ksDgRJxq/MgcMuoVKLHNkcj+a5xwgH0gaOpdfLTxEY7vySsW3cQMKGzWTNnkO7Teth03rnOEAbIHXiZK5+4enTF5AczK5aRxeLK8On3geAQW3AReNCVmVWq9/P+UIRegWFS438w/DXayDZ4cE9dZa8E1UqXDx8cOkykkLdz2jnjqbq62Hka32xDnmekH5XIbYgiVlD6AwGatzcsAcFYWjTGTL2EzC893m5lizJ2FQCOju88eh6fM0p9N/2BTaNjrAFi+jaNuKs9hqdlh5jRtJjzEhKi0uoLK8g/UQKVZs2ET5uLGP79zmr/fYNsylxkbkvYPyp4DKVqKK9d3uWpSzjhX4vIAoXv/OiIvQKCpcK5Vmw8V3Y+7PzdZ+7G1XX1K9dH9K7PoF74s8EWFJhzZ2kHl5K5B0/Om36rUzGunUYKyupHjoEz6FDqF7xC0efeQe3YUOwFRVjjIog4tqRTSpEUh+uLlqe+HQYvy44SsXOfKy6NqS2H0Wfdx4iOKbhVAtePt54+XgTHh0Jl42odd5mMfH+oc/wVguMmzj9rHPl1nKiPaIRuLjSEdeHIvQKCpcKq1+AxMUQNQQu/6RJxasjJr8Ik1/EXJZP8TdXEpW9BNNrwaieOILWtXWjZHPeex83QSDqrrvwio1l37wxGPesxLFnJSJgBvZ9Eg46PTafUAz+XsS+8ijGwOYV7FCpRFzbufBpgBfu1XaecowmOKrpWS3/yQ/zniHF08LrnlNx9zrbDbPUXMqwsGEXXd75+rj4nzkUFP4/kLQS/ngIltwPh36D0rSzz1cXwfG10PVGuHVpk0T+TPSeAdi73gSAIIDa0HCyrqaSu20bHmlpVI0ahU+7doiiSM/Zn+D93Rw8Pv+RwAVLsQy7GrW1Chx2PA6vQ7thEenDBpG/9UCzrllptvFqRjEAelslL7gH8P7iFS26j6TDm/nSsY6eJR5MvOLxWuclWSK1PBVZlqmyVvHWzrdYk74GWa6dDfNiQLgYJ9arVy85Pj7+Qk9DQeH8c+Iv2PA2ZO12vhZEkE9GkF72Jgx4wPn/PT/B0ofglt+hzfBmXy71j/eJ2PsmxZpg3B/YgM6j9QKYAI7EtQfAe84cAno07HooyzIHrrsX3cGNANg0LuiefIN2U8c26lqyLLNofw7vZOWT5SpyF0ZeHRRF6MaDuJhNHBnTF3U99W7PxUufT2Gx2zFmxb1F175X1Dr/w6Ef+GjPR1zb9lq25Wwjq8q5MTu1w1Qe7/X4BbHbC4KwR5blXnWdU1b0CgoXCnM5LLgNyrNh+PPwxHF4LhfuXAftJsDq52HGKPhygFPkA7tARMv84V0P/kC5yhevR7e1usgDmCZOBEBUn9sqLAgCMQ/dDICmXXskozuOtx5jz10vYjdZ6uyTmFXO8aJqAN5ekcQDZYVkGwVe0Xry+vC2iBoN9+SkUOniyoLNO5t1D4f2r+UPYxJDSwLo3HtCnW2mdpjKwOCB/HrsV2RkfhjzA0NCh/Dz4Z+5bOFl/G/v/ygxNz69wvlGsdErKFwojiwFSwXc+CtE9D99PLQnXPMjLH3YaZMP6AjDnoO+d4O6aQm8ziTl6xuItmeTFnE9XkbPFk+/Luw2Zyk9Udu4lXTu8y8AEPre26iCQzh0z/MYNy9k/7hDdPrtJ2RXI89sOE6G2UqJLJHkfoZN3ABxNTCzZwzRgadNUF2CnQXOPRo5hzOprirjmc3PYNQIvHzDd/VuGKtEFV+P/pqCmgI8dZ5oVVrC3cNZcmIJewv28v2h7/kh8QdCXEO4reNtXN326ibPpTVRTDcKCheC0jT4bqSzVulD+0BVjyjJstOY3kKs5QVoP44l3xCD3+PbEdV1R5K2lKSFC5FeeBGAwl49GfDjj6jrWd1LNhtJnbugiQgnZtWqU8ePfvILjm/ewRQYx6v3PsVuPx2CLONpgWiHiKdDIEu2k2GApV1i6BTkfta4+5LTmZBWzMjsVH6ZOqVJ858z9wXeti7hXdepjJ/yZBPv/jQpZSnMOTqHg4UHOVZ6jKWTlxLmFtbs8RpDQ6YbZUWvoPBvYjPD8VXw55NOP/gb5tYv8tAqIg+gcfOlElc8zNlkfToG1Do8p3yCe2hcq4z/N+2uvpqsgECS//c//OP3cGz5n3SYNLHuxoIAoogtM4vq+D249HJWk4p75BaOiSLCl28yZd7HeE59hp8mdq21upYkqc4Vd/c2EUzauY/FYW1IKSol2rdxXkV2u42l+X/hrRMZe0vtDdimEO0ZzQv9XqCgpoCxi8Yy6/Asnu37bIvGbAmKjV5B4Xwjy5Czz7mh+kVv+HUqVBfCLYshsPUKcTSEIIoUD3iRSrUPPlVJBJfuQjVjOMVJO1r9WqGDB9Hv229wuPnD3J2ULtmM5KhdZk9Uqwl68w2QJAree4/0I0lkJCQiORzkhrgiINP7yEEejTDWKegN+eHfHh2CIEncv2ozl81dzl2zFuOQ6k+TXFGUy32fjeaQVxXT3Ma0io9/WnkaO3N3IgoiNfaac3c4jyimGwWF803yevjlSuf/jb4w8GEI7w9h5ydatDGUHNuBfs6VVGt88XsuodWeHP6mvKCUvPe24aJ2mlUEMZ/gN6ec5XduLSkhbdKVOAoLz+pbaXTBraaacjd3ah54mBG33tjk68uyzONf/cT8tp0wWMxUGV1Z4a2me9dOtdrmpB9mzIbrALjL2o8H7vimRUIvSQ5MdhOD5g/GLtmJcI/gm9HfEOLact/+hlBMNwoKFwrJATu+dLpN3rMF/OJAPD+pB5qCd9t+ZPgPJLxgLebyfPSerZdw7MSag8jrCjGoXbH3N6CJP4psiyD7yV9ReYv43DYMTYA3yQNOV76qMhrJGnEZhtRkTHojxsGDGTFtKppmbKiC06Png3um8o4k8/uGrTwEZJZV8rfDpyzLlB3ZyK74h3nipDurZ43AQ/d+16J737T/e57d9wkVJ78nBoYM5P0h7+Ombd14haaiCL2Cwvmgugg2fwjHV0PxCbjsDaf3zEWCtbIY74LtlKr88HBtvYIleQfS0f9VTo0oI04OIbxPDNL4ruS8uABZcsde7kb+h/tQ+1ciqdWIdjvB/UsxhOTj5bWa4pgQDMEdGHL57S1OzyCKIloRNGoVyOCl15K85AXKzfuokTKxBFRj8Rag2ADA0snLmn0tu8PGD2se5su8TYTb7bR1OPByCeDt4Z+iUzXfU6q1UIReQaE1sVTBoUWw7g0wlTjTFQx9Gjpfc6FndhZFx+MJpprMLvfj1YoeOMVp+XgADPUltI8zU6ao0RD6jtP8Ur3rMCXzE3CUBuN+2XvY4l+m4PXFFOz6CdfCQ3TN344+byOHOkygU0zrmLYqbQ7UKhuFxx7EGlIIbiCYwbugA70HvkLwljtx0xjwDGw4N0591FQX8vjiq9jiKGMMRl4qL8Hd4As3bbwont6gEUIvCIIe2AToTrZfKMvyy4IgvALcBfxtYHtOluU/6+jvCcwAOgEyME2W5e2tMnsFhQvIsfxKPlydhJ+LGl9LJtfodxOSthjKM5ybrFN/v6hW8WfiHevM0ui973Oq+t+Cq3/zRO5MaqprUO+soEYU8GkfVGcblz4dcOnTgYIvVmDNdEXb61lC/KOJveptAFLy0wn6pjeGxXdienAbBn3Ly/QN79mFMTu/xCOkkOCK0UQPfxlRo0djdHrj9PNvy29Zh5m//w2u6/ZCk8Y+nrqOJ9c/Qqoo8aJPP67t+QB8OxRGvXbRiDw0zuvGAoyQZbkr0A0YKwhCv5PnPpZludvJn1oif5JPgZWyLMcBXYEjLZ20gsKFpqTayp0/xbMqMZ83Dg7lkaSbCTnwqdNl8qrvYPrmi1bkAezVzqhNO2rUrSCmAKmL9uMi6dCNDsYnPKDBtv73j8Mj9ihofan48ddTx6MDIkgd8AxtqtM4uu/3VplXmLcng10TqMSTuElfofMIOiXyAM8MmUGAVs2spMVNGnf1rk+5duNDlOLgm873c+0VMyB3v/Nk8MVVeeqcQi87qTr5UnPyp1GuOoIguANDgO9PjmWVZbmseVNVULg4cEgy987aQ2ZpDXPv7IsknF657Qi6EbnzNa3uxdLaFC9+FjsqTFfORO/evKyRZ5Kz/QTuh+3ku1cROqRto/q43XEXBu90KlPCMG/ZcOp4aP9pVKjd6L7qPvZ+O5mq6rIWzW3O4fmE2XZRoutSZ7ZJg8aN/v6dyDJbKKlMPud4VpuJrxZfz1OHv6OzQ8XicbPo1/NeZ8WvFU9DQCfwbdeiObc2jdrtEARBJQjCfqAAWCPL8t9JJB4QBOGgIAgzBUGoKyohGqdp5wdBEPYJgjBDEIQ6lw+CINwtCEK8IAjxhf9wt1JQuFiQZZmvNyazM7WEe4e2oX+ML+LLJSRfvZZEbVf6HfuATZ9Ppyj73IJxIfEp2EqFyofAbqNbPJa5yoTpj0zKVDW0u28QqiYUNPGa7gymKl9VgGx3+tq7u3gi372eapWBHjnrOJa4ptlzSyw+gWvemxSq47i+z5f1trs89gbsCNyx4lqO5G+pt50sSby4aBJfViQyQu3DV9eswDuoO2z6AL4aCHYLjH0HzkOe/5bQqNnIsuyQZbkbEAr0EQShE/AVzupb3YBc4MM6uqqBHsBXsix3B6qBZ+q5xreyLPeSZbmXn59fU+9DQeG8szOlmHtn7eX9VUl0D/fkgRGny/K16dSbdk+sYb/fFQwtno/Ht73Z8esHF3C2DVOiCkAj1Z04rKlkbj2GTlajHxGMq2fT3AhFD3fco49jswVhP7z/1HHBxQ8ZgRNusXTreWWz5nUsJ528A2NwoZoBnT/CqDHU2zaraDMj3GycMFm5buU9XLl4HKXm0rPaOOw2Plg0mT8tuTzkGse7E3+nfNVvFL83Dta9To5mEMXX7YSowc2a7/mkSV87J80uG4Cxsiznn/wCkIDvgD51dMkCss54AliIU/gVFC4p4tNKuP7bHaw+nMct/SJYdM8AjNqzfRnUWh3d7p9Fxk2bOGLoTr/Dr7Phm8eprqq8QLOuH0nvgSTLrZI/3ZxWBkBY/8aZbP6Jo8opQ4Lr6Zw1ZcVpGB0mAmqyKa1sWhZISZJ484P3+PPZe04dO7z+rgb7mEpWMNHTxiPhIXRz9yK5Iotrlp72lCopOcG9cwbzc00K1+vDGFcQivxhe4ITn0dTeZy9VZP5I+Ne/pxz8WSsPJNzCr0gCH4nPWcQBMEAjAKOCoJw5rb6ZODQP/vKspwHZAqC8LfBaiRwuKWTVlD4J7IkYU1PR7K0zir1THanlXDDdzuQAYNGxdNj4xDF+m3w4bFdiZo+h6OqtgzLnUHG/8ZRU1Xe6vNqCaJvDB5UkPXxKMpP7GyR4EulFqoFC1p989w0bZUuCNSgCgk9dSw8vBuJ479BJ1mxfDscq61xf9fj+QW8+tJz6HdvojxuEIHRf2GrdEM05tbbJ7v0IEFiJSUuw7hj+Ep+nryZdl7tyK/JZ0XKChaveoQJSyax11HFq0EjudP3LkLzZ1Km7ULhmCW4vJSE+w3v4R3uS3Vp67//WoPGrOiDgPWCIBwEduO00S8D3hMEIeHk8eHAowCCIAQLgnCmB86DwOyT7boBb7XmDSgoSNXVZN13P8ljxnJ80GAq169v1fHXHS3A5pAZEutLtdXBLTPPnefczSuAuBd3s7Pbm7S3JmD+oBOJm5e06rxaQugNn5AReR3+FQfwmHUZOR8NQ3LYG93fbrGRuS+Zve+uwqfMQFWg3Oy0ARpPOzJ6pNKis477t+mPVrYTXJNN4r6Gf3eZxSW8/Pab/PbIXbgdT0SccA0vPv0sHSMjEdQWJIue0qK6Hf7SCpyFTwJ9hp06dnn05QA8tfkpXsr7iypRZEbEvfQ9Ae4bH6ZK9sfn0cX49R+GSqPCP8KNwoxKAqLc67rEBeecfvSyLB8EavkKybJ8Sz3tc4DxZ7zeD9SZf0FB4UzsJSWILi6IOh3WrCykqiq0kZEIOh2O4mJU7u4IWueq0XzkCGo/Pyr+/JOSn3/BlpODx5SrqNm+g6x77yPojdfxvLrlOcCrLXbm7Mzgsg4BfDu1F73eWMPBrMavzvte+QAJnoF4bHwRn78ex9Z3LBrthY+UFHUuhN/2LWU5z5D/632El22n6MBKfHtcfs6+Gc9uRJRFBMAbHcXhdrpMG9rsudiKJbSGPFSBZ4+RGf8rAUB85ES6da17XmkFRcz98G0c+Vm4WExIfYYybMJEerWNPdXGTTOeGt3vxO+7grbhXxLR7rKz76doG/5AkOfpPDiXRV7G13s/xSLZsAsCN5dX0CXF6WNfpOuFesIbuBpP+5XojE4pvWSFXkGhtZCsVqwpKejatEE4WeKtYs0aCj/9FNlswZadjWgwIBgMOIqLa/VX+/nhNnYsCFD68y+njuu7dCHotVdxGTAAe3ExmffdR+5LL6Py8cFt+HCs6enYi4uRbXa0YaGoAwIoS04j0aIjPNgbuyRRbrIR7eeKh8E5L4ckM293Br/tzabcZOOeYc4arb6uOoqqrE26787Drma/3Ua3Lfewb/VPdL/87ub+Clsdz+BouPI95B8HU7Xn13MKfeLO7RgddkTR+YVbFVhN1/vqrsLUGGS7A6s1EpeAE/84IWPJSwRA0+cO1Dpjrb7HC4uY/eXnYHBDDIqk9+VXMHzgoFrtBlz2IXvWBVPGl5QUJNQS+pjAUZSl7SJ+/21Ej9iLKKoIdg1m+y17WTv7KgYlr0cvy+R6TsbzhjfxD6ydnEylcT7NFKRVNPdXcV5RhF6h1TEnJVGxciXW5BTMh5xbN8bevanetg17YSGCXo++Y0dsGRnYCwvRRkWhbRON65DByA4JJAe6uDhUHp7YsrNxlJcjuhgxJxyibMECZJsNXbt26Nu3x33CeFwHn/ZyUPv4EPbVV2TceitZ996HOjgIe84/7LMqFTgcJAV25OZ+t5867GHQ0C3ME0+jhhMFVSTmVBDmbeDJMe3oEe70Hs4uM6FVN91E0Xn4taRsfYvgPe9S0udyvP2Dm/GbPT94RHSmHA/cszcgS1KDOWYObt1ETvpmRk19BJ/NOjzz3DGXVKD3bt5K1rxmFeCGNvRsb52KigIGpv4GQFB4Dw4Vl7Hl8BGu79EVTxcjWWUVzJj5AyqVGne7BbNWx8EN6+sUegBRLYAd3DxrF//o3eYOZuWuJMi6l8KqFALcnU8DhWUl7N97DREaC5Xt+tNz2ov13ofq5Huiurxpi4B/C0XoFVoV2Wol54knsRw/jjo4CE1gEPbiIsqXLEHfsSM+90zHfCiRmr17MPTqibF7d7yuv/6USaYx48uyjKir3/yh9vYm+P33yX/7HdS+vminRCGo1Kh8vJFtNizHjlE2bz4uejXvXd0FrUpERmbRnmyKqy2kFlXjYdDw9lWduaHP6dQA8WklVJrt9I9ueoCRSqWiavDzRG+azrYNcxhw7RNNHuN8IQgCpcHDiMpZQtHhTfh2GlZnux2rV5K9awt6/yA6jxvBkd1L8TB7I+hUyJKM0MAGdX04crOBOHRdzg4wcnPz42DgQELzEnhv5gwMpVWoZIk3t23B7uWDuqQIvcXMoOtuYExcLF+++xYF1WZWzJvNmGuuR/yHL39QxGBKkr/gRNqrmGry8fTpSFjMCACyyg7jYjpIpaDC03h6tT57zkqMNUGk3/ICowc2XKs390QZAB5+9btwXkgUoVdoNSSzmbw33sBy/DghH32I+3jnVo3scGA5dgxdTMwpk01zEbRaGiMn+rg4In76sc5zSUfTYd58PHv3ZGyv0yu8yd1D62z/N19vdAZBPTwytsF29dF56BRsG+/DkLoWu/VB1BeBrf5vfMc/CzOWULjoWbShKygvLmLP+rVUFhZQXVxIdUEeWEyIBhdueulNRFFE18EL9kLh63spNRTR+eXJTb6u6O4KgG3Z/1CneENNCYx+DcE9iGNeQ/ktrw+uJRWI7Toi2W2ImenoC/OxurgSN2QoY+Kcf4uxk65k3py57Dx6nMrvv+Xau+896zp6ozeSXUDjYqag+hPyKgQ8Qrbz5/5H0VTtwig68I56DZ3aaSKyOqzIhz1wCHb69qidw/6fpCU4TY0ludVN/h38GyhCr9Bi7KWlVKxYQfGMGdhzcvG+Y9opkQcQVCr07dtfwBmejdpixgpIQtNMMMUnbfMdgptnphBUGnaGTWNQ1ndsn/0S/W9/t1njtDqyTOlvL+AG2EpK+G76zbWbIGAMjeS6J1/Ay88fgOirB5Gq2Y5up4SXyZfCg8nkLNiHx+BwIi+rK6ymNpJDD4C91Aw7v3YeTFwMfu04lH86avexYd1xDYo5OV25ViqD6LgOPP3yK3z4xmskZWaTmZfM3n0bCA9vi7ubN23CO2J0TMCsdqYitlu7sHTzUALVFooEd7wi7qNv9E2nxtOqtASMEChbo2bmT0t55J6baIjSvBp0RjXXPHNx+p0oQq/QbGRJonL1avLfeht7QQG6Du0JePoZ3EaPutBTa5CozrFsdfPFuGopvNb42qDD2/mzL7OMbzYm8+TY5tVaHXTnByS9sYke6d9TVfE0ru6tlwu+uVQc2UR4yVqKZV/yujxAYEARLj5+hLVrT2T7Tri4uYIgojecbZYQRZE2kwdypGgVbslGLHNy8CEA1llI3LQEVaCOyJsHgSTVacO37t5O2T4vdMZkXK66EcRrIGUD7PsF8g9xk8rMbMcVAFSWFpwS+rry1YDTPBYdHsah9CzmPv0sKnMNf2/xuo/uSKj/arQ6sBrGomUl3hIYwp7guth76xzvpikTeD9pDpqD3hSUFOPvXbfJzlxtI/NwCQ67hMNef7nCC4lSSlCh2eS99hqlc+ai8vQk5NNPMfbpXe+H8GJjff8RSGoNIzevanQfk9VBh5dWEuplYPPTI5p97QOzn6fr8c/Z7DaOwY/Pa/Y4rUH54c1I82/BSyglt+8bBI17sFnjJL6zFKFEQj/Cn8p9OWhKVLirTgtjuWspsY+ORutiQJZlUuf/hbgXBKoIfXYIKq9/fOHZraDW8u2bj+NwOBg9aCKL1qzBaLMR6e3D6HvvQe9VO73Wxl9+YX1yMnZHFRHRRnJ27sO1VMY1pJqYyzMwFXkieZVjQUtc24+xW0LJzyvBYZWx2x3YHQ6qTNWU5FdhLpYRC4242jwx9Kpm2p1X1HnvDpvEyu8OkXbQGQfg5q2nTU9/+l8Zjaj693LeKKUEFVqd8mXLKZ0zF6+pt+D/xBOIjdxMvRioqaohsDSXpLHXN6mfQasi0ENPVpmJaosdF13zPj5db3qTE+9twa/ywgeJV677nFChlCwxloAhU5s9TsdnrjhtUhnrPJa2cieVCXnIlXa8q/wpeD2esohyxCIZ92pPbFhZn7sM/3d/ZcyrM1Hr9KcHPFkMxUvj4IjVnVmbNiGq1bgCe6wW9nz6KUaLBX9JQuPhQU1lFVUOO+VGp43ds1piWN8xxN7+Gl9//zy2nbsAMPiWAZC3/j42LdIA+f+4ExFwQyUY0GpN6G1Ob6Agl/q9pFQakQn3daEsv5qE1cnkpFazf00G5iorI2/t0OzfaWtycaVYU7jokSwWcl98iZwnnkDfqRP+jz12SYk8gFaroVpjQMjLaXLfG/qEIcvwxvKWlVUo9e9LHOlkbFvQonHqQ5YkbKYCZEsVUkVWve3spc5zbpe/gsbFo0XX/OfTXOTYvnR+chKdXr4Su2QDwDPdA7cqD4q88vF9qCN+wSaOplZw6MdX6xxzQJ9huJeV41VcwqTu3bn/7be5umtXejgcBAkC2Votx61WSmQJL42WtpLEZeEBDF2/lrw7ppN8eDtXTb6f6rBQkiud79PUE/2p9PNE6FeA3wQbHaa5EDvQRIB7ASrJgkYyEavPw2B1bhQHxXgw5rq688ubSquYf/ccvr5rBbNf3snBrUUU5ZgAyNuXhmQyYcsvaNHvtTVQTDcKp5Bl2em+aLViLypC7e0NKjXVWzZTvXUrNfF7sOXkIFsseN96K/6PP9Zot8iLjT8vuwrXkgKGxNefkrYuHA6J9i+vQi0KHH5tbLOvn1tcSsVnwwghH8t9e/FpJb96U8ZfnDj0DJViOSatA0GSEWToFfMJblETa7UvfjESg2DC+ErOea+IVHA0mbLdGeCholINiZs2UZi6AwBRHUafHv0Y8OgtZ/nx24uLOD5wMK7t3AlbUjv1hCRJlBcV4e7jg0qlwmqqYcui/8GMeQTlWci4dTiXPfMFV35/PQW6RHzUEi91/okuPhHs/3Y1OYUqJAnypboLpXQcFMiwmzuctQFclVtGxrr9ZO1JJ7fcQLXKi2i3fNQ6NTVVDvxCjZSvWYdf8UHcjBKy2UzbbVvP+2dFMd0oNIhktVLw7ntUrFqFo+h0vpG/35iy1Yro5oaxd29cBgzAdchgXIYMuWTs8WfisNr469Mfico4QqFXYJP7q1QicYFuHMwqx2aX0DQjeAogyMeLw1HX0i71PY5uW4TPlc2zjf+N7LCRv/dVjpfMxaoHV7OaSKkjNkcV2eo09h57GP2RJxEAD0M7wju+RenXtxCsKiXDdzzh/0LZO9+2UezfvJfEFQuRHNUgaNAYfPAKiMZVNrL/eBhpj/zKhId64t6mDXZLFftun4DZA3za1+3iKIoiXv5OL6DE3SvJfP5ZIjLM2FSQ3caDtldOpaKqmhTNYZAEOplHsefTQnarKoHTX64R+hzC4zxxD/ejqrCKA8uTKPOOI3FLHlkbDmKSjWhkM7KopkbtCYAgBeEmlDKkj51Od57tqVQWA7nPrcVR43wtmUyoLuCiSBF6BYq++JLS2bNxGz0aXdu2iEYDKm8fTAf2Yy8oxPu2WzH26IGgvrTfLtkJSSTe/zBhBelUaw2oHnmyyWM4HBIFlc4MhQ5ZpiVRAR3GTKP8qy/QHvkNmin0OTseJ6d0NTbM1OgkXO0iXWPfwT16yqk2QcfncuLY64iCBmSZbMchCvZcQTdDORnGcQTf9nUL7qJxOOx25rzwPgWpW9EYgug2Yio9JwzG3cfpjSM7HPz88GLKrP7M/iCTn3reiklbCVMA1IwrymDc2u/pP/A69AbXWuNXFOUg3vIoEUDRY9fT7ep76OIdQI2thgG/DAIVqBxq7h1wF7u3ZyEBQwdAcK8odB4uuIScvbmu+/1rjmZkIUS1pdhmxEssRRI1qGQTMf4mgrqEEjykO0b/uuotgedVk9GEhGA5cZz819/AlHAI10EDW/eX2gQu7U+uQouxnDhByY8/4jp0KKGf/e+sc56Tr7wwkzpP7H3jAyKKssh54DmG33dTk7MtVphsjP90M3nlZrqHe6LXtGwVnJ2cQHdqqHGPblZ/R00BSVWLkXQCHhYDHV0vx3/Iq4jqswOxPGJvoGfsDadeF+yfSULRm+zq6YW95gCHVg9GFNR07vYxIZHNT07WEGtm/E5B6lYCY4dwwyuPI6rP/t0JKhU3vDmKr17YitpmYHBlIG3btMGgduOXjB2s9M5lRfYndP72e36+Z93ZG7dAweG9yMDem/vQbeg4Ek7sJb88h9kn5uEwOvcHJNFBtdWEm8ZMqUOHR4gn3h2ias21assW7EcO0T6snJgvmp9s16VvHwxdOlPw7ntU/rVWEXqFC4O9tJSc555H0OsJeuP1Cz2d80r64WRCEneTHtOVKx6oM/Fqg6QVVXPFZ1uotNgZHOPLT9N6t2g+R48lEbD6fkpUvnS55f0m9y85+j0HM95EUgu0048ndMRnje7r320aMccDyEpeSY39KIIgIRjTSTzwKiGR65o8l4aoKq1Ca9RzeNMyEHQEtr2c+W/Fo9aKTHmy5yn3Q1mW+XHRWtQ2b6q88nn/4QWn0hhMBUoLs5jz64t87RnPqzNu5vX7F566hqm0hF0zv6eoYyS2hGJWJbxGsbuFAzHlZASaCBZ8aSP2YLNjNfftu4PbeZsQOY/gQXVXgqretg0AY5+W/Y0BRIMB9wkTKJs7D4+JEzF2vzBFwxWh/3+KJTWVzLvuxpadTfC776D+j5dvTN0ST4DdguuVTQ/Tzy6tYfTHG7E5ZG7pF8HrV547JL4htvz4PIPSPqdG0GO+dhHGJgRNOWoKOLHpBrLVqWglkc6ed+Dd+ZEmzyEidgIRsaezTq5eOBmV90Fs1mo02jrLOjeZ3cs2sOmXD1Dr/JEdRQiiO0e35Z06/+MzW5nydC/y8/JZvOh3THIZqsAgHnny5lq5arz8Qrn//h/I++gKlngfpce815h01TMk/vQ9O1cupVwtohHN2AbFsVZ7gHy9s6rXFM1wXrnR+aQ69aX+7GtTRbbuGx7++DdU2roNb1KN07Cucm+ZF9Lf+D34AOWLF1O1foMi9Ar/LnkvvoQtL4+I2bMx9rgwb75/E89IZx6boi3bYVrT8tR/uykFm0PmvmFteKqZEbFn4pu+AoCSG1YQ2q5plTWTNk4mV5dHkCWENr2/QOfbpcXzAUB0pndY/Ud/sAZi0IfTY8gzePrGnKPjaTbNXc7BtcuxW03IDguSw5myV5YkjF4xdB5xLcHtYvEKMDLnlZ2YKm3MfG0F5d4HnVOQBQyUkpidQL+2/eq8xuPXf8b6ZZN5SVjArvs34FthRFQJDOk9iN5POMtR909aywM7HkVnE3nl1tPmyJ9f284Nn/Rnddc0Cr4awac3z8Pbp3bK4YplywHwe+ThRt97Q6iDgnAbPYrib7/F0KM7bsOGtcq4TZrDv35FhQtO9Y6d1MTH4zJ48P8LkQfI+eR/RAFibLtztj0TSZJYn1QIwL1D27TKXGKlFBDApvNsct8CdS7uViMdxm1ulbn8Tdder3Is8Wsk/VFwzUTWJrNj204uu3wfoli/TMiyzP418WyZ9z3W6ixUGm+MnoGo1BrUOhfa9e9PvyuH1+p3x4eDObo9l9+3zASgjSOAgbY41KjYlZwK9Qh9iWxCElSAnXC3AHp16UPXO6ejNZzOVz+03SjcNmip1FvZcXQD/eKGnTr3wTU/8PWyl1nikci1cyfwXNyDjBh1x6nzlZs2OQveREc3mCG1KQiCQPCHH5JyxRVkP/oYkfPnoW/bvPq6zUUR+v9nyDYbea+9hiY8nKDXX7vQ0/lXcNgdRKUcJMs7lMlP39nofqlF1dz6/U4ySk10DHLHzdCyzJvgXN3miIGEybl4e/s2ro+thsydj5BfvQOHRsBbbPwqu7EEhPciIHzGqdcrFndDbaxBlh3UJxPbFuxi55JZSLYUEFxRG4YT4OlHcJAD3wgf3AI8CBlc9wbkh7vT+UxVxef9rqNq6zqG206bw8Jdgmq1X7X7NxYemscO8QiiGt6MeYaJt9afaOyXcb9w/eobef6vp/kr7rT/fUhIHK9Pn8/YzXN4LuEdnkj/hE2lV+Dq5XTRzHvV+ZkI+eTj+n9ZzUDUavG+9VbyX3sdqerfz3CpRMb+P6Nm7z6sKSn4Tp+OJrDpfuSXIiq1CouoRmMzN8rTJrfcxK0zdzHigw1klJroG+XNkvtb7jFRWVbMzm8fIEzOJV0VjodrbTfBM5ElBynrrmH3mu4ct/5FhbqKQFsI0cN+bfFczomsQ3IIWEx1V0ySHQ52/v4Dki0FtX4ALh7X42qMRauSOZIWyPoNrvwx38GKl2edKjwu2+28s2wnPf/Yy2dyFQDzyqsZZXOanwq8ncfK9mZz5NCBU9fadWwzTya+TLx0lK6WCD7s8gYTBzacTbJNeAcGil0ocK3h9h+vxmQ6W1wHDr6Rt7q9iE0NH8++HwBbfj727Gx0bdu2+opbqqmhbO48VN7eGDq3bI+nOSgr+v84jooKavbswXz4MLLJRPmffyK6uuJ22ehzd/6PUFZcBkB1u84NtsstMzHtp90cyXVu5Hm7aHnjyk6M71x7hdkczB93p59QzmFDD+Ke+AtOfunYig6Rd/xrKssP4OU7lKBebwBQcWIuqexF1Mi0d5lMQNcXEHWe/0qgmou6Hzb9Mgqz9xPR7uz3iuxwsOWDB5HsmbSNCGXko7dhCAg8FdHqMNVQmZ7K0ZW72XM0nD+e/ZnIOCP7Eqv5ZFxXPC0Sw2tU9C+1c9UhZ7qAmk6+tLmyM7u/+5OY/CC0sypY6fszvaeNJb0gGVmA52Ie5pqhd9Saa328ecMXyHPvZ51qH5NmTcPFLQIvvTu3d72awVFxHNU6feB/dTtKhwXz6bdrDwCeNzQtB1JjqN61C8vx4wR/8EGLazI0B0Xo/8NUrltP9hNPIJ/0IgBQBwQQPuM7VG5uDfT8b7F75nxCJTtyef1FvctqrIz4cCMmm4NQLwMPj4zl6p6hrSaqJ3avJkZwXj/s3t9OuRWaCuI5EH8D1XoJtJBXNoeqNXsJaf84mSlfIapkBvVdh8Y9slXm0VhcPP0ps4PDbjrr+PGFH7J56SpKzVo6RWi57O3PEVRny4jKYMQzriN9Y9uhmzGbgwkubNnhzdFIdxAEKnXwWHggQTOOAiA90Jm2oZ4AjHr0Riorytm99C/aJ4RzYOFGoofEQDKUm8qadg8GNx4d9zHr5j1MrvcBsBwGC+zasAg2OhCE0+lfvojfQYelKwGw9m19f3dbdjYA2rCGi9ucLxSh/w+RY7YyP6+Ebq4GemzdQM7Tz6Dv0AH/p55E164d1TU1bHnoMUw33Uxm2/Z43HYbodGRpGVmY0Qmbsgg3F1bx7XuYmLwXTeQ+v0ntDm+l6KMXHzDa6/Qp/20G5PN0WqeNf/EbHFG0x6IuY+u7qejKZN330uNzkEH41V4RV/P0d13kKk/Ssaxu0ELkVLnf13kAUosBYgqmL1/EWlbfsQkWPCQNDiSS+ih1TJ+7BDirn0cQVV/0JigUtN9+q10czgoP3aUg3l5LAAcgoprU7MY20HH84ctiF8nkh/kgufEaHRh7ri5ezDipqtY9fEsItP9mbV7EQAqmhaglpqRw+QvtmKRr+Kx6Ku4cewYMsuLeWjVyxRI8fiL/Zk/5V2OJaeyJecw4BT63T8s4IrXG1+noDGY9u5D5eWFvksreUk1EUXo/0N8kVHAjk3bGPvBy+QAKg8PQj75GG14OObKSnZMv4/QtBSSR1yGz954fJ5+AhPwdzqno0YXiu+azrh777qAd9H66D3dybrrcUK/+5C0vYdqCX2N1c6+9DL83XTnReQPrJlN1633AeARddqdUnY4KFUV4+8IIajfBwB0m3AAc+E+0g48gxod0cPmt/p8zoXDYaVMXoloF1ho34NarUIrqbFobNg6yeyQZTZaF3D3Hj3D+zx0zvEElQrP9h3pvusAP+bo+bStDo0Ez49uh7rwOPZSC7bMSgq/OIDn1bFoQ92QTXbCR3VCNaeY6YlXYQ200L9bBOaaUvTGutMO/JNflu+gTDDy+WhvLh/VH4CO/i78dcsP/HUigUGR7dGp1fh29mJA5x6YH5rC9hFj8V6+kJI7b8A7onUSzVmSk6lYvRqPSRMvWH4oRej/AxyorGHKvhNUOSRiz7D/ab75Bm14OLIksen+hwlJPk72a28y+ZrJWCwWNi38nRqLlfA20VRXV1P2ww+0+fQj/vL2ZuR1U5AcdioSNyMa3XCPqZ0UrzThL7JfeRZ15yhinvketb7hzcULiUuwc+PZbnfUOrfleBEycFkH51delcVGckE1XcM8W3zd/ArzKZHfHXQzPfqeDtgy5azHqhXwNJ79u9X7dSduVOMLorQmkizx87abCFfbqfS4mbU97sbbOxCVqEKSJA4f+IXNqctZWnqIh458x2eSnWH9HmvU2IYhvej0TT7f7Xaag0Kv8YDHnfdePOcIpoNFlC08fqq9q4sVu9oKNi0RBhPZxU+QVQi28iD6D/kJT7+G3V0zK6yoZBUTRtZ21RwZU3u/Ru9iwPO1NxAeu5/dt09nzLqljbqvv5FlGfOBA1Tv3AWigMrNHVteLmWLFqFydcX3zsZ7fLU2itBf4hRb7YyJP3bq9U3DB6IduJzKyVeS/cRTJFx7PezeReyu7eydfj83XeMUGp1Ox+ibrjtrLNuo4ewYPgKP99/m8MrPkfflIZpBVskIQyKwFxYjWgUEo56AZ5+jYObnqBIrkRMPcnRVHzTDOxL20FsYA+svnm2tKEJlcEGlMdTb5nxQlZ6JFxDUufaKfX9mGQDtg9z4cv0JPlidhCRDvyhv5k3v3+xr7kgu5p7v1rL/ZFqWuGtfQnVGYrjSLKeQeIXVTh/8byFJEilFO8kqT8RV60li8v8IJ5syTQyTu7+EeEZWS1EU6dT9Vjp1v5Vba4rou2A4c4/Mwcc9jE7trz7nalUd1RbvAUmUbPME7GedM3TyxXSw6KxjQrUWwVBNWackLuswgerKbhQX7kLttpPtW66m/6Df8fSLqPd6giDiENTY7Q40msZJXfcxg/mj93BCd29AcjhqRejWhyU5maKvvqZi2bJa5zQhIYR+8z+0kZGNGut8oAj9JUypzc5l8UmnXucN73byf/5sfutd9G+9SchH72EXVeyfNIVrHrqvwfE0Wg1HOoUyeON+HInViIPD0HfuSvWStYgb0hEiDUhGEdWhQvLueBxZliBYh/fjd1MydzaOxQmkrpqI9oaB6ENjMMS0xy22L6aSNCq2rqFy0TKEo+VI3iL6if3xGj0FQ0AMZfGr0UfG4dl5xDndH6uzj1KRsBGvHuPR+4c1+ndlr3B60nj61X7s355SDEBiTgVzdmWiUQkYVCI7Ukt4f+XRZteH3ZFaTBkuvCvdzNPiLAo/H4P6sS0YXJxPPmXle9EIMsaglicSy68uILMyk54BPU4J7omydOLTF6PVuDIpbhoqUcReVU7KuteoqjoCDpFDuiRCfJ11TssBXwlKjH25qu9PZ4n8PzEafYmyy2xTW9i2+zXer0hnbL8nGpyjdf9eyrar0Gjz8LntbFu1sYsfxi5+yDaJvMNrOZ75Ir6VV9B20mOojcaz2h7Z8wNZwpts3ziVcVdvrPd6x0ttuEimRov83+g7d8KwYzXHdh4kbsC5AwpNhxJJu+EGcDjwve8+3C+fgMrTE6nGhNrfD0GtPivH/oVAEfpLEEmWmXYolZVFTh/nqwO8uCHo7Hwpg68Yh330cNKOJxPZJorO//iw1Idt+gSmDruZF3qKTGo7znm9O+zYKvPReTrDxQt3LSL/jTdRHTOhu2skwRPuJ3jC/ZQlbCD7uSexz9hKFVupAgrPGFv2F1Fd1RHpaAa2H7ZR8MO2U+fKgWx/ATHOH32/ngReMR0Xv7N9mSVJIu36axAL7ZTxCY5YAwGvvYRf9yvPeV+OzAwAXDxqexv1j/ZhX0YZc3ZlAjCmYyBvTe5M7zfX8vWmFKYPbYN7M4Kl7h4SzSdrj7NQN5nBIR4MSPuCPV/fQs/HFzvvWc7DXfJosQhkV+awIf4mAuUMfk7qiKgLB0cFAZbt+OEU8R/NZYwuU5NS+hUOLzuCBmQdhEiQd0CF76hbqTQXMKLjU3gba6cFqIuZExewec+XvJS/gdIGqlgBmNf+QdFaV0RBwufWrqij6/ZTFzQiRdlrsBmLiB35AGpD7fdt+563k7NkJWr3PZiqyjG41p2TptShIUg01XmuIYK7OMv/nVi8nHb9uzX4pGI5cYKshx5E5elBxMyZ6GLPeJq98HXfT6EI/SWG2SExce9xDlY538DjfD34rH14nW9GtV5PTOeOTRp/UFgPXi+3UiSXnDomqtSnRB7Ar88U/P6Ygr2yFLXb6RWyZ+dhuC/ZSWV6PPbSIkxJBzClJKHx8sPQoSt+g69DVDkFszJ5DyXbl2IryMWla1/Mx49i2rwLeX8h1k1/kv7RcqQOHqj8vQh7+gOMgbHYqgsRC+1Ivf3QdIxCWrSbohuepSD2DUI/+AyPdk4zi7W4gIyvnkUV6E3Une/z5ysfE7P7LxwI1FRU4e7lftY9P3FZW3alllBcbaXKbOeW/hG4GzRM7R/Bd5tTWbAnkzsG1Z1KWJZlFsRnklVmptps47ZBUYR5OcXpWH4VIDPRtoruqT+CANp+d57sJ2FRO9BZrU36+/yTDZmbKTn+KIGUkquKwygV4l2TSDleZOkGM6T9U+w8+AKRZV9xHFCpVMTpnyFo2B3Yq0r448lJ9NhYQnmUD2OufL5J1/b1a8/EIa/y0oKhbCvcyw31tKv69TfK9nqi1pbi99BgVL7+9Y6Zn7iOAv1vuJq6oDF41tsuNGI8WSXxJO7+il7Dn6mzjSSo8NZKTbgjJx1G9mdFaDvaLJ3NnynJdHnjBcLa194PqFixguwnn0Ll5kbYjO/OFvmLDEXoLzF+yik6JfLpQ7uga+VHwsyqXMCHefllnKheTHcPb7p7hxHrVVvozhT5vxFFEY+oPhAF9Bhf73Xc2vTErU3P0wdGAvc4UwSU7VpD/m/fIO1LQT6UTubaa5DcwPOFewEwDO9H9LT3sNyWScbMF7HN3UHm209SMXYC5XMWIaRVI1rBBthuehnjst8AUM2cVUvk/57zwnsH1Do+JNaP7zanMmdnBskF1VzdM4QeEaeXaTllJm75fifJhaejLr/fmoYAdArxoLzGykear7iKLRw09CR46g90DnbalIsOfoCkEvBz6fnPyzYaq91C1fF7cMGOd4dfGRnYE0lycKIsheFeMYiCQFpqOhHP56Dqo0bqPpTek99A6+YUWq27L/0e/4zKjTdRlXL8HFerG7PDgpsks8Feyq8rH2BUz/vx9mt/6rwjP4eyvX4IVOL3wMAGRR6gpiQNgNDgWxtsF9vlJlKXfkCR8DMbliaBJGK2pxAScg2d+zlNlDWo8XJpelUnURQZtXQ+K557h6hVC6mafDkLe49m1Icv4+nvg1RTQ/7771M2dx6akBCnyEfVzmt/MaEI/SVGgdW5ifV795hWF3mAAL0HHpST4/Dkh3JvfigHVXoJLwenMjSoIwU1ecS4B1NurWFvSS4xbp7oBSi3Wejt3wG9umXl0gRRxKvfGLz6jQEg9/evKfl9LkJ8ARVPfwWAW4e+AOgCw4h97keOWW7CMX8vVTt+hiA14tBoHLkFqA5VsWv1NgKqisi65T5GD2hapsieEV6oRIHkwmqSC6uZsyuDMC8DX9/ck192pDNvt9PU0y3Mg5v6RpBfYWZBfBb5lWYSsp3BUcN1+wGIvG0G7oFOkZdliYPF3wAQOuCLZv2eZFlmbvzjhGKmwOdexgQ6vzBEUUVbb+fKUpYkEl9/k7CyChxX/0z3gX1rjRMc3YV4FxF51Uaqp5fh4uLZpHm4uAUxd9B7TN/8FK/nb+R/yzYwXOVFr8AhDEoZhpSfAbTFPa4Elf+5U26UVG9EUGnxjak7V/zfiKKasKCHySl7B4d6EwAqh0Be5UcYD4XgFz4Cu6DG2615m/46g44rP36Z1APXk/DJV3TcvoqjY3ZS3K0fEUn7UZUW4jZ2LL5333XRizwoQn9JcaLGzK95JbQx6OjrcX4Cm3oH9SLppJt5ankmu4pTeSvDzku5PpD7dy7xnJP/GgDLqb7eSVsZaizkhvD2DAlqON1AYwm68h6CrryH/L9+pnjBz7iNGIVfvylntWnz3HekeT+J6O5K+C2voVLrODTIaWfduP0Yk4HCHfEkrNlK59GNj3o06tQse3AQS/Zn08bPlVk70jmQVc6Ez5wFxUUBPrimK1f1OB3t+MCIWEqrrbzwewIRPi4UG97Da/19JP31E71verXWNURt0yOUd+fGcyzlY0ItO8jSD2dS3D11tts4ax7R2zaz7+bbuLEOkQcQ1WqKI72ITCxm68/vcdm9Ta+oFBE7nhVtxpJwZCHv736X5VIpv+f+wfv5MXQytUUlFqDr0risoSqHEVljx1ydjc7Np8G2HfvcSazlOiTJgSiKmGvK2b5tFCeOf0iN4Pz7u7YwADCqazuifviErXOXIX3wFm0yynAZ8BJ2TSKhb587huBiQfg74dDFRK9eveT4+PgLPY2LitQaC5P3ncAuyyzo1ob2rv+ee2KxqYwFqduokRx4qLWsLDbR0UWkr6c7a4vLCTcY8VAJzCmo4aA9HIA+6hP8Mbhped9bC1l2cPRkMWnd7G8puP1RPKzV5HkE0H7RrwSGNmw+aIjNxwp5b1US2WUmXrq8A1d2b3jj0m63o37jpGC9cjoFQ9Jf48kSkhgx5DCCunHpcNenLCEt82fCHfsxYaTYZQRXdX8bN23dG+2/Pf8K7RfNJ2rfXvSGut8vCavnon7ImbExcPMavPxaHqKfl5/AdctuxCToeDnrLi67dhCauMbtFdXk5bBr3+VoRE/6j16FKDZtE3zDH3fgcN3Agp23srK8J/OujaJfjw7NuY062fTKZqLNgFrAb1ondNGerTZ2SxEEYY8sy7UDXmjEil4QBD2wCdCdbL9QluWXBUF4BbiL044Vz8my/Gc9Y6iAeCBbluXLm34L/39ptzmBcrsDN5WIRhRY1C3mXxV5AB+DJ/d0OG1vPzOt1LjI0/+/ta3MZ0dW81Z+ALvsMTy1ZzHv9Wx6RaeWIggquDwGlp1AV7CfmNWr2DrtftqmHCB77GWwcnWzxX5wWz8Gt218NS61Ws1hdXs62I+ALMPJTXNXjy5QkcT2tZ2JcBlByOCGC3QvSXgf18Kv8caDYp/7GN3uDrz0nvW2r7RY8di8kfSOnWlfj8gDlJfm4wNkDG9H+1YQeYDAgM7Mn/gro/+8lt+8NzD0gHejhd4YGEyQ6gay1F9TXZSCm3/T6gcMHPs5a5cOJUPtXMnPOrgDjYeNnm26Nvk+/olkdRBhlilWCQR46Sn66TB+07ugDb54AwX/pjFGXgswQpblrkA3YKwgCH+Hmn0sy3K3kz91ivxJHgaOtGyq/z8pPxnJ2cHVwOLusf+6yDcFQRB4qMMYvmvjnPPPFVGY7ZZz9Do/BNzurA5UfXA3voE+dP/MmWJALTnQa1tW1LsplJaW4CJVUoA3Zz49+7e7H2+TEZPWwVHbGlbvfB6r3cLGtBX8EP8ye7I3siNrA4eLT7Ds4Ju4Fn5NimYQIwdt4dqujzco8pIkseLdjwguyCPo5psbnF+fSc50F7pDya1yv38T6NeeSIsfVaoqjMMaH3QmSRIltk2obK4YveoPhqoPjdbA8HFrGehZgFFtYtnRAD5a1TrWgcysclQIeDlk3G9uj6hXUfTDIezFTXfh/Lc5p9DLTqpOvtSc/Gm0vUcQhFBgAjDjXG0VarOxjzNYZ5CXK+1c9Bd4No1jfOjpIJMfkndckDkULvkRAN+xUwFI2er8sJf4BJF2yOlhYjNbqEhNpyIn/7zMobK8hNwvJhDmyCZ/5Cdn+ctr3MLoPiEBbfhcvEutqKrnsXlTB+wpDxBeMYuypGlUH7uD3ANjMBTNJFE7jhv7f49rPWaav0nLzmXRA4/Qec5PHB80lC6TGn6Azk1JAMC1snZqiJbS1tCeAm0ZYkDjnxRKk3dRYzhMiPF2VJrmvd/1Rneev+tdfrvH+RQR5dMyBwGLxc7qN7fi+PYQZmQORbli8DfiO60Tsl2maOYhHFUtc5M93zTKbUMQBJUgCPuBAmCNLMt/l2x5QBCEg4IgzBQEob5MQ58ATwENOrQKgnC3IAjxgiDEFxYWNtT0/xXtXPT08XDhw7R8VhfVn2b3YkIlinwX6fTD31D67692ZFnGunE/UqAG766jAOg2bgglRk8CCzPR3Xc7x39bzt5+g8geN5bsEcNYcu+zjR5fkiR2ZBxDkiQO5Kbx5oY59P/hWl5Y+8NZ7RLX/kIH+2EO93qNzoMn1T2WMQj/JC3tjldRbfUgR9ODqtB3qA59F2vkZ1QEPkuO3/NM6/cJLuq6La35uXks//xrVo2ZgGnkCNqvX0vK9Tdz+bdfnjMYK3mL80G81Ld1yuadiYvgQpG6DIej8V8i+ekrQRYI7za1xdf/bM1GVIKdG/r3aVZ/SZLYvSmdrR/sokOlxHFPDerbOzB+ujP6WBPggu9tHXFUWCn6IRHJbD/3oBeIRnndyM56Yt0EQfAEFguC0An4Cngd5+r+deBDYNqZ/QRBuBwokGV5jyAIw85xjW+Bb8G5Gduku/iPM7tLNIN3HuXhIxks7RlLjPHiX9n7653+5rFyzjlatj7ladtQpzvQ3T7k1DFPP2/CZs9iz8z5xC39BftzT+AOmFRaDA4rgr3hD+mLf/3A/sJ9PNL7VmYfXMvuilmo7EHYxUIE0Q4iLMk+QsTuYO7q7XQNFU8G/LgE1F/6b2hwGPvH/Y9uS27EYnGhzUMLGnWPsixzZN9BEr+bQaf1a4kGMtrEcuLWaXSaeDkTOrY/5xgAI+5+jSMfLcDk3vpCr1fpwAHTf7qNd674AF+/gHP2qbDsRSsEoHNrWVhppamajSneDIwooWN40+z8f/PHN/volV6DNzKJES6Mubd2zIMuwh3vm9pT/HMixT8fxvf2Tgiai69wX5PcK2VZLhMEYQMwVpblD/4+LgjCd0DtbD4wEJgoCMJ4QA+4C4IwS5blhg2HCmfhplZxd5gfryXnsLqogpjwCyP0hTUWtmSWkltlYc3xQhKOF9OznR+TOgUyLNwHT61AbmUVs44kM7fKDjoDFaYjVFUV4erauPqorUHxCmdqX98JZ7/Nwtu3Ifz951gXEUrFwUNogoNxHE4k9uAW9EcOcmTDTtoP64tUXQ0GA8fjEzny1Uyk3GMcG5BGWoTII1vWnxpPJ7rhr2nDLR2v5aO972FXZ5FVkceuzBSyK3IpPvoZsaKIS3jDOcjLk51+4IV9HqCx5cdXPv8Kkb/9SifArNNj++gTRg8f3KhSif8krZ0nvjlV527YRB684kk0q7XMrfiNa/+4hs/6/4+OHbrV274mP5NqQyLBcsPBUo3ho+W/U2XzZNqg5pXLXDlzP73Sazjupab/gz1pY6zf/GOI88brmnaUzk+ieO5RfG5qj6C6MOmI6+Oc7pWCIPgBtpMibwBWA+8Ce2RZzj3Z5lGgryzL9dbgOrmif6IxXjeKe2VtFuSV8OCRDBZ0bcNg79arDiVJEkdLa2jv7dJgTo+Fx/N5cs4+ZJPzMVwGBL0KzKcfy63dvJECnJvFeoednlXxTHP/DMFiYPSozWj+hYyVks3C4fF9wOqg44aEc2ZULMjIZefL7xIUvwkXm4lCVx/8qooxqbUY7FYsKg06hw27CHlLfuWnA8s4UbWTD4a/xqCI0wI+YObNVKoO1BpfLct83+UhevS4u945lBamYZ45Fp29hrI7NhAdWHe6hb+xWK0c7dELrd2G8bfFhMW1bZbA/82yawbRJqGY0o+fpM+om1FrWmbT/ifr9q1jzbwkPFV6rp9wGe5h7niF134PH13/Dtnyd3SL+gWfqNqRyk3hyVlfs+BQGAkv9cfN2LSng92LkwjaWUCOFjo83htXj8YtrCq3ZlO+NAVjrwC8psT+67nnW+ReCQQBP510kRSBX2VZXiYIwi+CIHTD+ZlPA6afvFgwMEOW5frj3xWazFAv5wdjaXYJKxNyOZhTzrgOgdzXtfEZHP/JL4eyeXHW/rMPqgSuHBpJjLcLIW46ViYXseVIAVWFJlQ6FdeNiyXM08DYSF8i3XR8eyCL5JIaFq5JRp9ewbVBIsNDAxnTJgLoxYoVa9Dq9nPs2Bo6dnSm47Vaq6msKsDdLQRNK4uKKT8FVaYVe5yhUR80//AgrvjhEwpSM9n5zWw0u7ZxJLoDWhwIwaF0eugWasZfgVqCUTGdGB1bdyDY3Mkf8cP+P1BhZGnqQsJd2nJTcCCvJc/g1oTPiNj/P0Z7tufBK+fVygrp5RdJ4rCX6bjiHk4kbzmn0Ou0WkwGA9pKG5LV2iKRB5D7dIWEdXg9+j6ru//M+LkbGmxfYzax+8AhQgL9iYlo2DPmyJE0ds0tIrTCaT5Z9osz26qrVqRLrwA6XxuLWq8mZevXZEsz0Fui8QyrU6uahF7rgYCEVt00P/yySgtuO/M5poG4e7o1WuQB3AaGINXYqfwrA9GowXP8xRMxe06hl2X5IFArV6csy7fU0z4HqCXysixvADY0eYYKAHhoVGgOlPBrXvapYwcPFLA1uYgfJ3VFo2rchz2vysw1i/ahU6s4kVDHprdD5vd1qWcdEtQCfXsG8eqItrT3Odtn+N7uzgAp04k0lqfaGahzOynyTnx8R1BRsZ+8/EfJyPwWWc5DpysFwGz2IiL8KTp1urZRcz8XkiSRv+w7AHSd6s6OWB/+UWFc8U7t5Fg2h42VQaC1Q/sGvjgivPx5ZbgzYdmLw288dbxtUDvWHJnH5qL9zKg8Ssiqh7h6XO20B6Kn8wtbay4751xPHDqCxm6nxNOL/o20xTfEuEc/YVfEVxSuW0XbDSmUnEjEO8bpsVJdU8G+Q1twcXWnfWxP9BoDP89bhmOHD4eo5Lfozbj76Rg2vCdxkc4vKFmW2Z94lA2/J6LN8kajNhBxvUhXTSyFKRVYyy2knChn27ZcEnbnMexmN9JqPsHF0onuw2aiamEaDYBIHx0yIl+sWcdjE+reCK+LhPXpRCHgc1kEwcG18yKdC/dR4UjVNqo2ZaFyUeM2tPkLsdZESYFwiTBnezqqPBMeHjqen9yJPkEejP9+O1t35XB5jZVVN9cd4n4m05cnsGpzxlnH/CPcubV/BO/PS6i3n2yXScqvrCXyZ+KqVQEOckurzzrep/f9lJZOYvv2+9DpE7HZjIjCJNQaV2A2ObkvEhraH0/Pln8gUt6/B9sPm3FE6mnz1GctHg9Ao9Ig9OtB6OK9ZJ/YT0hMtyb17xgzjo4x47jbVMr0+aN4tWATo2uK8DCevWcRGzOAapUB46FfYWT9ofV7N2zB8ejDGMwmxM+/RNQ0PX3yP1GrNfS/5kE2pRewL7Ua7bq5GFPasv/XhYgmOxq7cxGxQiuh79AWKas/groaW3gJbilhOFJgzc4UfvfchSRIiCYNbmYfJLWequ6pXD15BG38navbsIHOSOI+DomkFWls+jOFlb+mEDbEk54930Ln2jq5fW8fPpHlCTP4YkswNw3MJcCzdp3gfyJJEvaEIkzIdOrfvOAxQRDwnNgGyWSnfEUaokGDS5/m7RO0JorQXwIkZJXz5vIjjO4QwJc39Ti1ej/82AhGztxO0qEirlmwlzlXdat3ZV9usZ0l8tdc1gZBLfLeEOfKd2S4N2Pfq7+IQ3lWFcfLa4j1qO3HvX5/MvOTrLRxsXHz8NoRiF5eoYwf/wcmUxkqlRat1ojDYWfVqpUIogWdrukrp3+S8/vnTpEf5EeHb/46lQ65NWh3zR3YF+/l4LyvCHnhm2aNYTR48Ui7m7jt2A88tGA8P92666zzapXIYa/OeJgK6h0jPTcf4aEHqPL0wv79TPr3qD/a01ZtZu+788g4VIQu0A93Hx0Dnr4SrWvdpojVD9/Lofws8HSFv/YD+9EBNZ5qIi8fjamqkqzde5H3n0Ct1THkuan0aH8FlVXV5BUVsWbNLkiTUYki6kAHrmE1TBzbnTCfulNEiCqR9pdHI2sKWL9YpCr1ejwmtV6qAlFU46rTIiCjUTXOoyjx493EVNpJ8NMSq26+OUwQBbyvaUuRyU7p4uOIRjWGTv+eM0JdKEJ/kZNbbuLhefvwcdXywdW1TTQvjmrHtIxd7N6Ty+NeBv43qvajfI3VwWe70wHnhkr8i6Pwczn7zd/W82wBd/HW8+1NPZBluHvWHsb3Dqkl8uWV1SzYeoSvtmSiQcWPdw/GqG/AO+GM/OIZmdvQ6YuxWUdgMNRdOKKxlO/fTOlrXyBHaGn3vyWtKvIAbToPZr9WQPPXDsruzcHTp3lFo3v0up8rkpew1FHC6q1v0b/L7bi5nV5pOnSu+JYmYLKYMOhqb1wnbdxCmNWC/b4HGhR5gOVPLSTbEY7a2xe1xUZmgRvFz/zOlZ9ei6qOxUB+dhaowadjFOkVOcgCRHTvyaPXP3NqD2CtaREHsn8gqF0UPdo7TTturi64uboQe1fTo1gBOozpx8H9P1GQ1BGbxYxG13oeZYXVEOxWhncjXDVzK0t5w/g2ZW0qeLpjw5WyGoOgFvG5uT1FMxIonnsU39s7oo9pXFHz84GS1OwiRJZlvt+Syh8HcjiYVY5WJfLTtD70b1N3Nj+r3UG7V1cj2ySCI915YGgbbmwfTE6lmSdXHWHbwTxkqzNeLa6THytvrjuAJD6/grwaKxMifRrcyNx7Ipv7f9lNrsUpqAbBxhNDQ7hjbO9G32NJSSb79g8DoE/vrbi5Nf/x9tCNAxCOlRI6ewYe7RqfnbIp7Jj3KR6vfE3K5V2Y8MH8Zo9z+Ohirtv5EgBGSeaFmGu5YrDz9f4dc+m28h72jvqUHoNuq9V3zYw/aXdIRjaVIHqEcsxiosJeSmCojr5PX4XDbCN7w34cNWZWrnXQxpjFyKfHognwZ9urc9iXG0g7nyJGvXn2nsixBfNYunAWgizz0PfzULvV9ojJS8lj9nP3o3fxZ+r77+HWip5fezesZvs8Na4BuYy7awD+oa2ziXnFRzOptoqse+a2c7a9+4/32V76M6IsIgkSN+mncMfwe/HzP7fvf0NINTYKvjmIo9SC312d0Ya13u/tn7TU60bhX+aR+ftZsj8HjUpg+pBoLusYSM+I+lcDWrWKz27tyZc70zh8pIjnftrH89oDyA4ZHDIu/kZuGRjJQz3DMarrz/PSK6BxJpTn524l1+LCxDYa+sX4M7FvHK5NDOLy9AzBZu2NRrubxMQ59Ov3WJP6A0g2KwVrf0E8WIJ6fJfzJvIA/a5/mPUzZxOy8iBHrl1J+z5jmzVOh7jJfFRyHLvDzPwTv/NC8q+k5sYzNHwEhYc3IANe/mcHWB1PLyV1Vw4RaW6IekDv/Du1M7ixudLA3nSZY7fPxaJ2w6Zx7qOIDivdru+LJsCZvK3/i9eTc+8sUvK9kGx2RI0aWZbZ/d7bbN67DaNdov/A4XWKPEDm4SSQLXQdc1WrijxA96GjsVlWE/97AKtn7uTml1pH6I1aFcklDacptthtfLh1ATsK/0DEh/VXL+GNP55ltnkRa5dtYPXt69izdxsbUtczMHYIA7o2rb6vaNTgd0cnCr4+SNEPh/C7pysa/8aV9WxNFKG/COkd6c2S/Tl0DfXk2fGN86q4PMafy2P8yauy8NTao2w9mIdfoJFHRsRwXbvAVvPplSSJghrn/x+7oheRgc3bPBNFkcsum8Oq1d0xmxdis9+PphHpes3lOcg2C9m/f4J55lpUJRKym0joA7VzvbcmsiyDxYLODjVFLcuNM3rAUwAM63EvTyyaxHemVL5L+p6rykZiED6h5qgvew8l4ptSid4iEVLpIA447q8lfHJbov20mPKrKPvuGIPd1BywFFNYA17qCjxcyymtEBlxXx/8up8ubSeIIm6uIgWVOiRJRgRWP/oAh3LT8ZAFrv/0W1xD69+A9PDzBCAnKRkY3aL7/yeCINB3zBgOrZ9DeU4g+zaso/uwES0a83DWCXZne9EvNLfBdlfMe4RcxyZE/Hiuz8t4e3jw0S1f8tHCN/mheh7vzH+FBeY/sAsOZu1bwNj9w3h47JMEBzTeeUDlrsNvWicKvj5A0fcJ+N3bFbXnvxv0qAj9RcjN/SL4ZO1x4tNLqbbYcdE1/s8U6Krj5yu7wpUtT8taF4IgcE17I18fhlV7jzN9/Lm9fepDFEV8fe6movIjdux4l8GDXqqznSkvleL186nYvgF5Uxqi2fmlJUS7YJh+Gb4jb8QY2nI3w4YQBAGL2mnmLMxLQZblFn95Glz8+GLqNgrK0pi79AumFDjjDNjmdHvN8lRT4aqmtIs3XfuFMdLv9OrU1dWF6jFWzCvT6KrzwRzsg8VFg1vfQMIvi6zzekWlAmrBgmS1sfaVZzmUm06UmxcTv5iBWlf/l2zO8QyWfeosSBLctmEf/5bQcag7+1dWs32BBp+QA4THNv89/Ef8ZiTZn4cua7hSldXhTEa2c+oq9Gd4ME3uex2/rVnOXMtieoldeHPsu3y5/lP+rFnLvuWH+HTwJ7SPbTji+UzUvgZ8p3Wi8NuDFH1/CL/pXVC5tm4MSUMoNvqLlEfm7eP3/TncNTiK5ye0njdCa1BRVUOXN9YxMUbP/+4c1aKxHA4bq1YNRlRVMXLknrNW9ZIkkTnnFao+WYBYBbIGGBCKOioIz/bD8Zt4278afbh+xqsEfjDv1Ouym8bQ8cHncfdsfH76+sh6ZjMAfq/0przSjiRDoP+585yXHC0hf3UajlILniY7JiD2nbrFbc1z8zlW4keIbT3JVfvwkeCWOb+haiBobflnP3N062KQYcx9L9NpaLfm3F6jKcpN59c3juIRnMdNzzcvFcKfezdw/6+VeOmr2fTMFFz1tTfnrQ4r05a+yP6yFbjL7dl2e+19l6KifDLy0+javheqk0Fuew7t4LFdT1AtmHgx6mkmDWtaDIgltZzC7w+hCTDid3dnxCYs4s6FYqO/BHlmXHt+3//vJwRrDAadBgFYfsLE7Sdy6B7TPC8UAJVKg6vbGGy2WWzY0Amb1ZuwoOmof1uO/WAq6iQTBGhwf+wWAqZMR90KrpjNZfidL5PadyQ599yPd7EVz9mryJ69Ctv6P/EJqm1XPlyQyftbf8ZD60OYhy9qtcSx4jQOFO0n0qUTSZXbMcn5qGRXlvIyIiJbl6cyYkrjn06847zxjnOazw6/tA2jpf7kbCNeu5qse5aQZnFmFJ30+vsNivzPT79JYdp2tMYgJj/9PKFxkY2eV3PxDYrAN3IHJZneJ0sENr12wOK9qcj4s/qxkXWKPMA7m2dzoPxP/OSh/DDplbrn4huAr+/Zm7E9O/Vjvv98Hl32EC+kv47nbi+G9m68KUsX5YHPTXEU/3LYmQTttn8nCdrFl2ZNAYANSU5/6gldmi+i5wuNRsNtnV1wILI0/kSLx+vd6zFcXR5EEC5DLLXDw+/BgsMIFTbEyZ1ovzaekBufvKAi/zdRnQcxcOsBQnduPnXs9YWv0+n7fnT5fhg7M48CcDA3g+uWXU18xTz+KvqCH5NfZUbS62wq+oVyKYkD1XMxS2WEanvjqQ7j1d6bSNPY8Yhvfopug8X5JFAfKrWK8XfGIjkKUUkibhGRdbZzOBz8OGcZqfnOILrrX3n9XxH5v/GNUOOwulBakNXkvmabgzUnnBvQvu51P2mllBSxIO1TZEnP6ls+JdK7aT7ugf4h/HDjLLwdHsw9PKfJczS098HrmnZYksspnnfU6TRxnlFW9Bcp25KLcdOp6RraMh/z88UL1w9hdsJykgsqWzyWweBB376PIEsS8ePHIhab0L96G1HXPdUKMz0/uHn4Uu2ixiHZWe+6C9EeiqTJ5c5116CRfLCJxciiyFt9ZmDUaimorARU6DUqJrXvzYG8VGK9w3DTnzZV/ZUXT1hmDYWF1fj5Nb2otdlVi1u1jcqsCtxCa38pOmx2NqzaiCwVE9v1MtT/KDFosdt5bvUGFqjdsAaF4jblftb37oxfoGeT59ISqkrLQXRlT04qXfQuBHo2XojVotOUZ9SY2JFxFEmGuQlrOFGeRJE5H5tciV02gUZiqO/NaBrwQmsIvd5AX21PdlibZ2J26e6PVGOjfGkKpYuPn/ckaIrQX6QczasgxMuAXZLRXGQpTwFUKhW+OgcZZa1XmShv23Zc0zKpGjaMjhexyAOs+eQHQqvt7GkjIMgBrL5+PpvTEnlj93NIkiuIxYwPuo+J7everO4RXDtHfeSAEFTzT1D54R6O+mno3rEcQ88eqPzOrm8rm01Yt69G030AgosnjrwcKuetwK3aGcR06Jcl9Hn0OlRnBK+VF5Uw65lnMVdm4+LVnlFPTK91/QXx+5ht8MW1uoIou0SSqxsnyqyE/ssR/MlpAdhkNXf/Wk2AyyrWPn45bsbGLXi2p/9GUPvXqZQF7lp/xnvTYcAoBuEmeuKQvBgeMo03R7csHXKMRxtWlKyjuLQAH6+m1yB2GxiCVG2jcl0molGNx7io8yb2itBfpHQO8WTR3iyGvb+BPx8ejIehdaM9W4P2fnr+ynLw9Yp47hnX8oyDuYsXYwBCb2t5PvLWwlxjQqfX1arUJC6aR4GbL9Effs6W6La4ag1c3XkAV3feAEBmeQFhHk378LfpHsTuCgvq1UeIKnSlbIML5Rv34neND4KbJ5ZtWxHdXajab8NqCYNVRwEbzuqeHdF7pXGi3MTe9Bj4chb9H3PWAUrZm8Tv77+ELNXQacTtjL5rcp0ZL0vtdkCDQ2UkSa/GyyTRJ7J1cs80hoRjRSxfcAy3UgOlAVZGheazNjmAJ+Yu4qkJw4nyj6hz3g6HmfSM7ygu3kB1+X7eCIN3c+KI87qMnKp8buo8gcvj+qARW/cz1Na/HZTA4bQEBnuNbNYY7qMjkGrsVG3KRtSqcB/VvAjjc6EI/UXKm5M7Ee3nwvurkvhgVRKvTer4r+e3PhfvTx3MkLfX8M7GfEZ2KSY2pO7I3cYib96MXaMhoFfLvzRaiizL/H7Xk8RuXUGhdzBujz9Br6vGIDkcrP9uPsGFGRwbfTWT4up2AWyqyP9N76GRVK15ijIeRBt8HEeeL4W/liFjhpNlSQRq0LunIZhyUAUHo3JVoY0KQjfoFnwkiT33baC6wrkpm52UyuL3ngXZSnjnsYyZPqXea/9SUgNuBkx6pyz80acdRv35l4htu3NY99txPErtuAA1oQaefXwIgmii22urWXU8gFWfHAYO08k/n9evGkX3SGfW1IzMHzl+/E3+rlSqOfkRuSXEzC3DHz6v8+4a2xP1ERW/HPiZvp0GodU0vUrX30nQHKVmKtZmoPLU4dKr9R+hFKG/SNFrVNw/PIY/E3L5ZUc6D46Iwd/94ioh6O3uysAwHasyZDzrSZbVWGSbDWNFBSWDByPWUxu1tZEcdsz5yZgyj2LOSsaSm4EtNxd7fjEpCRBXnk1yREc8inJwee4Rfl80HH1RPhHphzGptHS+8/wUSkvr2ZuO+y9nq+oGek+8hdKlmSBo8L6pI9gsqMPiEL3q3mgURJEQr1wyC31Y++lcDmybDUCHYTcx7t4b6r1mlcVCjtEDnc2KRaNFb5OJ9W36PkFjkCSJg0eKKCoxc/xAIfZDZRhEIM6dSVPaER729/6CG8sf7M6R7AyScnL4PcHGoYIA7p+9naX3FpGU9CQmUxqCoMbPbzxtoh/HYAhj/vYbCDbvZnfqPHpH1VsLqcV4efvykP90Pir8kkfn3s/HN3zRPLEXBXSxXpiTSpFtDZbWbjaK0F/EZKcmIZam0DYg8qIT+b/xcdUDJnKLyvDzaIEwCAIWvQ73bdvI3buXoB49WjQvm9VKWXoalpzdSAUZWHMzsefn48gvRi6shGIzYqkDwXH2U5KslpG9VNSoI1kYM5Swx6cwvNcAVt//LG12r6NGa+DopFsZ+tA0vEOat2o/F7FjprPn2DoGFMyjLOoFAt9sWmqH3pe34c9ZhRza46x4NeTmJ+k+ruExckrLsKvU2FUQUu0g20VFvz/2UqyBaLvIyx3DGBDd/Cc2SZJYuS6drJQySo+U4Wo67WlS7q1h+hO98POunRqgTWA0bQKjubwnPH4F/LL+Fz5Yr2Lr7qm4aEwYDOH06D4Xvf70KnhCjy/YtKUPJVmzzqvQA9w+/l4cS+18WvIt36z8Hw9e8WSzxnGUmEElYOxxft5TitBfrOz4ipCVz7BQVrM29rsLPZt6kXAK5cHMErq0qTslbWMQ1Gr8v/qK4jvvIu/Ou9DMmoVvh7r9yU01NVRkZFCRlkZNRgaW7BzseXnIxUWIpaVoyyvQ1dSgks5eHclqkL1V4GNA7BCIyt8HdWAguuAIdCHRGMPaoQ+IRaXS0MZSzfiP5pO6ysqm43PoeE0fol98gF7RMagbWeSlueiM7siugThqRLSGpn95hgzsx03ROcR/tZzdSRDmW4G6gackSZJYlngURA+QJR70NfB2kYk0N6dHyjGrxI3HM9jkYSDcp+l5WpJSS1n0xQE8qpx/D4daRt3bB39vAwY3LZcNj0Bs5O/0xiHXUVY0GReNCR+f4XTrOqNWGze9D9kEEmo5SklNNt7G5r8vG8OdVzzIqu/X8mfhaqZW3ImHe9OzVBq6+lG1LYfKdZl4jGv9ylSK0F+MpG6Glc9gcwtFrMxjdPxdlPTqiXfg+dmoaQkmiw2AK3o1tqx1/QT374/l88+ovv8Bjr36CoVTplCekIA9Px+KihHLStFWVKCvMZ0Sce3JH4coYnVzxe7piSM2FrO/JzbPNTi8ZDoM+gxjWAe0fmGNLrtn0LlwTU8v3lsH61KCWZcCn29N4rsbchnVxMRWzcFiCEQjOEhZN4N2VzZ9lWgMCiZq2DB2J83CVJJXbzubJBO28SCIHkSUF/JKu3DGtW/HpGoLV/51mC5aHde08efG9Ewm70zi7ahg9CqRftHeaM/hmlhQXMO69RlkrMvGKIO+nx9DhoUREeKOVtM8t0aVSou/SyU1dneGd/623nadIu/ElPEGe9LmMLpD81bZTWFqzM28kPoGYxaOobuqE+9d+RFuHp6N7i+ZnHsq5uQyzodDtSL0Fxt2K2x6D/SeaB7aQ9KxJGJ/HUr6rPvxfmLZhZ5dLY4X1uChknF3aZ3C31HDh7MhOJiAAwfhwEE8AEkUsbi54fD0RG7XDqu/P9qgYAxhobhGRuIeFYU+IOCszery8gPE71kFgDauN3rXphd+uGfURII81hHp50tRZTV3zS1nT1oGo85PGqGz6HXza5x4ZwW6gz9DM4QeIGvvJgACug6pt43ZfjqSdusVI1CrnALs5aJj48TTFURfKqjiJbGUqXnOJGGhx7KIv7w7pRYbdk6WuhQEqiusLP8zmeT4AtyqHYgISCoYeX8XOndseaoIALVQQ5m9U4Nf2r7ubckEHPaqVrnmubhi6DUEeAbx2f7P2MJurlwwide7v8KAnsPP2ddeYqZk7lHUfga8Jsees31zUIT+YqLwGCx9GDK2weUfg0ZP2w5d2GUcTN+qTfz1yTSG3v8N6lYoH9daFJvB30CreQSZzWYOto+jPeA5cCBd7rsXrZ9fLffGc+Hh0RVvr8coKf2IzZvvYty4xU2eiyiqmNzXGd4uSRL+f8zj53g9wV5/ctOgsS0uyt0g1mr8pQIOu/YjshndJZuVg/tTCPdRYwxtV287N62GKxxVLFW5sjbxMGO71F38/O6BkQzI9mRPVgVP15SQ5SIQunYfDhHkk397lUNGJcGAQhNdrBJSnDudegUyoE8QOm3rSU25LRIvbVKDbfQqp8mrrOoEmaWHCPHo0KK/l8Vh5Y0Di0ko3McDnW9lVHBts2KfroP4pesgdu7axDMJL/Dc/pdY2209alXD914dn4dsceBza0c0vq2zYPonSgqEM5BlmZoDhZSvScd87PztgNfCWgNJK2D2FMg9AOPeh15OH2hBEOgwfSbZ6jBGli0iadeqf2dOjcTXAJnVIlJDsfdNYNasWZT4+KB67ll6v/oKuoCA/2vvrMOruLY2/ps5lnMiJ+4OIYHg7g4tVkpb6nLrbvf21t3t3rorNWiB0qLFXUISLIS4u3tydOb7Y4KVOIH24+Z9nvOcMzN771mzz8yavdde612dVvLHMWTIvUjSFLS6IyQeW9J+hTYgiiJf3jgUL0Mjz6yVeeqXH8+qvfaQk5mMCw2YfYa0X7il+uu/od6qZvC09knn3hwzBI3VwtuZbVP69g9w5aZRwazrFYqbWcbbBAtMWu4wO3C9Wceldh12EXb20vLou5N58KERTBkf1K1KHkCt0uKorsLexj0X6NafKrsWL9M+Ug/OZ/HWUVQ0dJxSwWwzk1CVj9lmY1XeIUYsns5vCS+TUbyGhzdeyZx1/2ZXaSbPH1rJK4eXUWluPFF31MiJXO+xkAp1NXmF2RwnjrTZbJit5hPlZEmmel0WdVvymnecOyqEnhH9KajbnEvtJiWvah0gOmnwuLEfuuDu51iRbRaEzK2QshaOLAVrAzj5wJXfQcTpD6ezqxeJkTcTkPginkGtj87+Cng5ajhWI2Gz2dBqz26mkZOTQ35+Pq6urkydenZ85Mcxbux/2LlrFLm5S4nud3YeGANDonh2bgW3/FiNydo6edjZIqaohidXJbEZmJTxNg2N9+NocO1w/eq0g+z89Tf0apmwOXe0W97N0ZGophqSHN2oamjAzbHtBeAhwa4kBbf8Atq1Kp5qjXhOF6xVohqLpKWtOaQoqpk6bhPxmYuoqkvGs3E3G/ZcRK8+L5GY8SFaTLi4jWfekDdPq/d1RhyfH3qX+oZkBNmMoPFGslWB6MSM6GeY7BXCVymrySr6jbvX/XGi3pLET/l65peM8AwFINgnHGrgki0LiDCFYNS4kCpnYRNsPB/1FEmlx/imZjGv5jzAEHU/VC7ac0pb3KPom1G3PZ/aTbkYhnhjvDgUc3YNNWuzqPghCZ/7h6By7r4/QbbaMb+5EAfrNmXHwKth4JUQPAa0LXs1CBrFvdJqMbd4/K9Cv0B3thVW8unvO3hgYeeiAyVJYkdeIb4aFVH+fqxYoZhXFixY0G3yGQxGkIei0cRgMtXj4NA+9W9b8HX1AapZkejNO90j4gncvjqB1JI6ctKqcBYdqdQ7426vI+HA74wedyO0Yx6z26ykrfqSzUtXIiMz++r5qB065iWz0NOZZy0a1iQc4/rRHU8JeSokSaJCDd5tquCzg9VmxUMTQ17TFESx7fO4GwKY0f9JAPakfY6Q+wZV6f8m+Hi1quWklswjwns8+dVJHM5dgq3wZwZgINtjKl4Gb5JK9+JkCOPjiS8w2F3x3pkfMor3E0ewrzSJBaHjKGis58v4p7lz62McWKjQHc8YM4dPdQbWZaxllbweL8mdUeJg9soHeDTtmRMyeo/vRcCUsec8GPJ/XtHLkkzD/iJq1mWhH+iJ28I+CKKAYZA3am9HSj88SO2mnC4tkshWO7JdRjwlulCWZKp+TcdWPxcH3TZl36UfI7RDx2qXlRFS7tavcHZ/AqP7ufG3PRU1jVYWfraH0jozjWY7VrvEtaOCGRzkyle7sujt7cSO1BoA3olvIqN6A7dPH0D/ML92Wob9uQW8mJhBnIMLaruNxxKOUV1djbe3NyEh3etd5OU9naqqGJKTVzJ48LVn1Va/oN5MCdvO1ixf0ouy6N0CPXFHkFXTyCs7Mghy1zM2wI3C2iY27so9cdyzdxD2GasxfTOD0ZseoHTni5QtWER01PhW2/zizmtpqG9Cr4brnn4O175jOizPQE93KGyk0mzp0vUAfH60AJuDisnacxNoBbAveSsa0Uagb+v90BLGRtzBhvwPUMuN6IIex9MpjOxjd5KScAupSOhF0AO9dNBLVwuBA4n0v5hfsyOptIlUNhbR6OKDodlN9YHoS3gg+pIT7S9PW0ZlfQoWux27zUpOeR7jhk5h3NApPFz5KC5GVzQqDbvjt/LW4bfJUCn/9cgpU85LxPv/tKJvTCinemU6Up0VXW9X3K+MRDhllKD1c0Qf7YEpqRL50rYzCsmyDBIIzQRk9TFF1KzNQrZKaPwckU02nMYH0Hi4DEt2LYbhk2gyPY0+/WXsmUmoe/dvU9YBU68iKekHxhZ8zbGvjmH897mx1UuSxNL4fLanlLEno4LqJisalYBRr6G83sKPMbn8GKPcpMnFdQgCBLk5IJvq+D1DYFVGHI9N8CK8XwBZNXVc1icMT4P+tPbfiDnE+00CsoMLU631bNE48Zrgxs1qDQsXLuz2a+oVPo24+FcoKd0EnJ2iB+jlpWNrFtQ2dZ65s95q54ENx9i8Kxeh2ST7zSnHVz06Ca1apJeTA2pRJOfuA2TE/cyo2LfJ3/4mtKHo+/YLIm5/Klc+9kSnlDyAbDYB0GTruknqvbxSMKh4Znhol9toD1lFcQSoYHz0rE7XNWhdsFgsjI+4HYCC6gfQFL0PQKU6gkD/K4kOupLF+24hPP8lvs3fzBfcg1lw4NMq8EnfzrLBEUS4Bp/RdqTHYGJqYhj2w0VcF38XzhZ3Mq4uYM7kyXi4n/Q2GjpoNFOFi8hI+AIB4bzRmvzPKvr6mCKqV6SjCXDCdW4v9AM8T1Pyx6ENcKbpSDmVS1JwGuuPLsQFS1EDtRuykUx2QMZaUI8syWCTUbk74DTaj5q1WYgGNWo3A1KjFXutherfM0AlYJwdhvPEQEw/KGYYqSATOSgCoY10bs5Gd4LuWg7vR2KXuo8x8jhkWeaNP5L5MSaXOtPJh33eQD8+uFaJUs0ub+Denw5g1Gt4cnZfkopqmdXfDycHNWarnZ3HcnluxWFe21mBpU5G8tHzxq4ErndSk9LQRJ1dokiCQr0zIeZ6vh4aSbSPFzes2sRGJ09+GDmDUTWNXNI9XngnYDSG0NQ4EAf9dpKTlxEVdcVZtVdQrYx6vVw6Fyla0WRl2OubwKyEmU0cHYiETFmNGbss4++mp7+b42kPf4hXECGzHuFo1gb0jW1z1fsG+sH+1BPbkt3Onk0b6Dd0GO5eLc8AJUliw69LecmqB99gwj09iCsoJtLLA+dOrrlUGZRZaTsWlbOCSipEEgU0ms6b4CyWStTqk4nNp/V9kHSPkUhITPM+GTl8+aivWL3/JsbYdhPtqKVf5LNsKEjkzVIPJhys5CqnON4YfBEOGkfKa2r5T0w8uywDafK4i/CcDThbFCK4pH1FRIRl4evtgYvehSNlR3h0x6MU1BcwI2QGjwx/5Ow7pIP4n0slKFvt1O0qoG5LHpoAJ7xuG4Cgbn3hyFZpouSDg8hNNgSNiNpDj7W4AUGnQtSrsdeaMQz2RjRoEHQqzBnVWLJrAXCZHnyCjU62S1hLGk9bdLFsXYZm220IgowdI+IzmQhtuGLFLHqSUVkfkTD1OwZMnH9W/RB3pASVSmRItBcVDWZu/TaWQ3mKGSba34Xn5vWjn59Lqxl6WkNNQxPD/rMdi07FfeOMvGNRIwsiarsNJ5sVZ0FmukHNC2OHoVWdNFetTErlkewyGrQOHBjbHx995zlD2kJdXQU7d01Dlo3Mmb39rNr6bMOPvLbFFQBvxxq+vn4ijVaBr7YkEuphYFCQK7NGnUlC9+XhfF5efBjfUCN77hjXro35VGT+dwThtamYny5Hp275P0lf9Qm//7AGwduLmoAIHE1NNKJCI9mYO2sWpcVFOBtdCQoLZ9E3X6MSBCQZmkQ1S0ZOo1Z/Unmq7XYeN6q5Z9iADrslBq6Ow+ao5lk3d+4ZfOao92xhtVlZt3kYMgLzZhzstLvk5i29UamcmDzpULtlJcnK1m1RGAy9GTNamT0fLk/hyZQs4i2+TNOlM87szZsmNSYHAwOyc5h6wIaLteWoWJO2nhLHbI4MW8tL419ihG/X1kHaQk8qwWaYs2uoXJyCvcaMQz8P3Ob3alPJA6jdHfB/ciS2GgvVv6YhaEQcotxxnhiAaNAg26TT2pCnB2NKqcKcXo1hyMlRlKAS0fqfPgrRTrkCW1A/1D+MQUUN5l9eR3fN063Kog0aClnQmHcYyT6vw2Hjp6LRZGXRoqNYD1ZiA+5ytVDSzPwX5KZn2d1j8HHpui+v0VGPp5cjZeWNPDp+JBOzctFq1Azw9UbTRhj+JX37UNTQxHN1MpuycrmuX/cGjjg7eyDLnghCBZIknZVP9dT+o3hti+LHXdpgZO5nh08ezK6B+BpcVqYR7mRDo9UR7G7gqYXjeG9/NgB3jgntlJIHKOt1MeEHU0ncv5gho69v0eW0RG5ELdixlJXjUFWLKbAXOBiwCiIr1m88WXDHLhBU6JGQZJnBoYE8MGEQS1OzSalrwF2jZlmtmZfrHUncspdPpneMa8cRgRqrxEifc5Msp7axDkdNA1mNs7v0/xmNQ6mpiScv7zuCgm5ss6woalCpHLFYSk/sG+QZyRrPSB47sIpFNb3ZDARaKng91IPaWBtFVjeQ7SComHi1JxXOTRRkl1NX04TDfndCLP15ZPq1eHcikUp34X9G0VuKGij/NhGVowbP2wag62XssH1M0KjQeOrxuuPMrO9/flEIgoA+yh19VMd4vNW9Tyb+lh3bpiftP34embvDGZX2H6pf/IxytQ/lwbMYed0LbTI+yrLMngNFHNxVQENqLU52AQE4rLNRgoSLTs1903pz67gwVN3gFldTa0bXvAA9OqzjI7vttU2AjhDDuSFwk2URna4Wk6kWQyfcFf+MCP/e/HGfRFFVKTf/qMyCLgkTeXjecA5llbD9WAEJRSZS6jSI2IktN1P2zVZq6tSIQKRn580OoeNuIztlBUM33E9C3JcIw26hqq4coTgBY1U6QY25jLPWMrCfHvW/09DqHHni6+9QFRewYNIEDu2PYdioUaxb9wc2jRZBgMeef+m0c9w97GSwlEfsIV6qB6GNGb/ZZuOuHXH4ipBrNlDjqGKmoGO437lR9Ct3v0GwDgb36ZpXVv/o99m9ZxyZWe+1q+gBVCoDFksZxcUr8fU9ufD6bPRUane/Spo9kvEHMxg7/d/UXuHIsjeOIopm7nh/DmpN8/M4TPmK9cli/6os3PSd58HpDlzwit7eYKXqlxRMadWonDR43j4AtevfhwnSmpKABrDJXuhm39JmWY1Wh+9DWzi45XusmXsIrD3I6KwPiXknkZH/+vWMUV5hWQPJmdVsXp2Ob5lid7c5qQiYHMDFM8P57a3tUGdj030T8PbqPFlVS3h5dzpNVWb6DeycV9Af6dlskzWMbapifGjXgoTaQk1NITpdNlbLyLNS8scRFdiHqMA+XOv/FumlTrx98w1otU6E+XuxYNzpC+sjn1nBjkItIgovUJBL5+MNfDxDqHsghvj1r9L/0GfoNipc640qPTnu/UnyvAiT3gPnkFEM1yt26HHDh3Pwt2y2Vdbx6L8UGoXIAQNZ+u3XjJzQOi0CwMrSajC44qrX8f3BBAobmigyW7GLIjf1CUWWJO44kkmRwRlkQCvhXl3NaJVIgykcR4fuM72lFaYRn/I7vurfKTUPZlqfrsVY5Bf8AIDeIbBD5aMiX+FIwh2kpr14mqLPSFnKFSwhL3cc2YTz2bvvcP+/H0cQUpFlLVmHMokY0ee0tkwNVkRRQPyLssVd8IrenF6FKaUKlasOz1v7/62UPIBgrgDA7jkWdTtmJACDsxtD5j8APIAsy2z9z3VMqV/Drs8eQjXgHo4kNlGRV49DpRVdc2CvLzLaPi7MvjaKIN+Ti1G1zUE/lbWmblP0cbnVAHw6r+VQ+j8juayC5w4ms11twNFm47WRZ86augN5ebsRRTs+vjPPqp3GxjK2bVtEQUElFosNbaWB/tpatNrWR+k/3DmO/66MY1+5mXI3V0bvT2b3kF6Eurt26tzODo4Mm/8KZTOeIL84nUCdCk+/aPq2Ysa4ZFA0sat+o/LYUZg/BwAnoys3P/jPds+Vq1IU9ddWDVTbAS2IytrS0tQSAAS9E2Ms9RxGQ3BxISOzkijFwisHdqDxDGH+zIkM7dN1V9n6plp+3f4oAdqNeAEVVh/GDn+jy+3ZbIqXVJMpl5qawxiNbZMWeXlNQyEPUJSz3W5l27aHkeT12GyOzJr3FNe/t5dCkwtNXyzFRfZAEDSEDQk/oy1BEJAkub1QiHOGC17R22uVUZTPA0MQDX8fjpjjsMVvQA0IjeWdrisIAiPv/JiEjxeiTrcSf7gQACNQo5bRSQKOQ92ZOTsc/xaSRT8/JZJ71x3l49UpvP9g59zxWsOkcA8OHS7h5Z3pfD67bZdRm93OZXHJVDo4MtVSx3/HDMbXxbnNOl1FQMBoSkqhsmI3cHOX2tiy5TN27FBoAvR6O1qthNG1ir5RUS2WNzWZ2bvlILGxsfR3NCBPGczvNmUBenJ8KofHD8So7/jAo7zRQm5tE0N9jeyy+XHDb4k01GxgZH9vvpo/EKc/UQ0IgkCT3oBo6Lxf+9IhvUkorUAQwElvINjoTJCLE5V19XyemEaNzc5VYYFMCTs5+7LaLmH17kPsjzuAUJ7B4p9LGfrMvzp9boCth1dQVfQyfpoaCq1zGTvgTqb69T0rd8SoyBewWqspLV3NsaRHGDN6Y5vl7fZGQEKlMpCR8RkpqR+g0zVhsw5lzOh3cXb2Jd2eiozI4myJ29WlqOwBVBWV4xV0uhnW1VtZ9yrNrcM37NyYttpCu4peEAQHYAegay6/TJbl5wRBeB64HTju8/WkLMtr/1Q3CPgO8EXJ9fW5LMvvdZ/47cNeY0bQiAj6v+c7TZ2zDADx2q5xzjs6uzLgsY3seOMTyAJNsMCM64YSFtL+zTRzbCD9t6SxsqiSJ6ua8HU7e0Kl+4eH8N6KY2zYkUPG2HB6ubY8U/jqSBIf5ZdTqXfmYUd4bMqEsz53a7Dbbezddwc6HXh7T+5SG6WlqSeUvNFo4uGH2x5ZLvl6Jcm5B05sN9ZX8kDkfOr3HeIwasqdXdmaUs6lg9s3I1SbrMz/OZ6cJGX2d9elffl0ZdLx7HnExBfRP76IkN5ubL9t7Gl1VUY3hNJi7LKMqhNKsr+PF/19zvRzdfNw442JI1uso1GrWDBpGAsmDePVL5dB/lEOZ+QzqFfHTCWnIiv3Z0Icq9H5fMYN0e3z9XQUfr4LKC1djcHQPq22SmVArTZiMuWTnfMmOh3IsoCvzxW4uipRsgEONvJNWqY1GVHZ3ZFlM85uZw6qApvX7OLWZjPrrgHdshbWGXTkbGZgqizLg4DBwMWCIIxuPvaOLMuDmz9rW6hrA/4ly3JfYDRwryAI/Vood85grzWjctH+7fKtHoe9WfHI8lkQqMkytVVa9GINt97Tr0NKHkCjUTE5QnmYy8oaun7+U/BRcuGJ39cuiW+xzO78Ip6qMFOhceBeA/x7+Lkx1xxHVtZ+dLpUZGkOAwd2Lf1fVZVCiBUUZOWBB15qs+zuTXEk5xxALxgJ9VZudy/HII78uJqIA7uYcSwWgLuq2p/FxRZVM/yNzSeUPMCnvylKfuLoQC6bdtJMkJNexZbsk+UkSUKuqUIlS+edvTAsJAiAQ8fSutaAVE9JU2/Gd6OSB6ioVFxrfX0uaaekglEj1+HmNhZ//2sYMngNVqsHpWXP8exXnzD06RWMCjTgYxOIaJ6pCYKOb75M5IPX9/H+GzFs3avcN64+BobNCiEnoYLC1OpuvaaOoN1hrqw42h8nddY0fzrkfC/LchFQ1Py7ThCEJCAAONYlabsAe60FlbF7fbK7ExIGZNRogoO63EbyL7+TUx3GkOgyVK4+nao7PNITjubz+548BvRp2+0r++BWSta/TYgpmQLHfnjPe46gqJNuu3+UVfPf7w8jAO5Bzrw++8x3+tbcSu5dlYja28ALHlr6OrueW7pfwGhUrksQun6exYsVbvchg0ehUrVuAiwvrmLLzg2IaLjrwduxWax88PExyhvzKEPGQ21kVp8hLG4uf++6BP41OIRwv5aJ8746lI+tyc6MCcF8MWcAK9JKyKpuQqMSuX1gIKIAga56poa4M/+D3dz10wHW3T2WXm6O1FmtONbXYgvtdV4HOmv3HCFl9zpMaJk1rvMpIVfv+4YAx1RKbJ2Pfm0PdnsTAKLYMe4qBwcfhg75/sT26FE/sS/mUoYHfsSOkntYnh6C859cZaXkuhMv1oPZKdTWmpl/US+sZjuCoCj9840O2TMEQVAB8UBv4CNZlmMEQZgF3CcIwo1AHMrIvaqNNkKBIUBMK8fvAO4ACA7uvmALe40ZXUj3s0+eDWS7HSQbiGrUlXuRBHe6lm9HQXJCE7KkYcRtnXc7mzjMj8DfjvJDUiGPWAbg0AKlrCxJ7P72KcbnfowHBjKchhFRH49lyeWU3LYZn8DeAFzs5QpaESwSq24aScApCcPtksT0b/aRlabcIuo8MzvEAwxQ/07K3NeJHNm9I7dT4eERjtnkiUa7hpKSu/Hx6RwDaENpDiqVDbtdzYZVCQS5jcAr/CSfT/yeozjodUQPiSB+71HsgoVJIy/G6Kos0Aa4RlBQnYZRMnDvkw8iiiKv7snkSXMtyx3s/HE0gzSfQS2/8JqHVP8Yopg/FkSc+SL/54hQAIYP8Cb+QDHT3tjGr/+cwKBmN87Gs+Cv6Sxsdol9G1YgAqMnTsXbvXP26JT8o+gbX6bMHMCscd2fGaqmRpllurt3jivnONzceuHt/zzV5Y/yzOj/kF4dSo3ZSKH1Tl66fgZWm8SevYX4+Ttht8us+uQw+Sty+GhLIWKdjX7j/XF2P/8OIR1S9LIs24HBgiC4AisEQegPfAK8hHIrvgT8B2jRP1AQBCdgOfCQLMu1rZzjc+BzUCJjO3cZrcgtydhrLYh/oxG9rSAH+atLUUmFWDUD0NmSaYp4iq5YxxsLM1j+/MOU1ihmnx+ejsMzKJSJ187H6O3aoTZMZjsmScYkAH9iVqitKiVz70q0CT8xvimeBMex+N/0JYO9A8g6Fo/nz3Mo//YKTA9tw8FJOd/cEYGs2Z3LlI938duto4lyMyCKIleuOERWWhWuvgbenxtFxo9PcLOssFWmJiyDc6joRVGNh+cC6uu/oKnpZMYhu6WJstQdVJfE4eQRiV/0Jch2K2qHk4uXhbGrSKl6nBFD1RQmX05unZWPvvuMUH0wBhc/imvzqGxSzFVrVrnh4qj4SeflnOQ+v/neq/j9xcUE2V3IXppG+FWR3DI2nH9IEhetPUyCo4DZakevO1PRb08qRWVQM8a3fYX58Nhwrj+gpA287L87AeglhjHG6fwpljW7DiIi02Tw5dKpo9uv0IzEnBj2H/0MT00sBjUMHPAuXq6dt+23B53Wh8bGTAqLlhIUeEOX2hjYbx6LN+3EXdxIb9dsAEKiQhFFEZ1WZMqkkwNVn+fHsOjpvTjUWHHydGDkvDM9cs4HOrVCKctytSAI24CLZVl++/h+QRC+AFrMcycIggZFyf8oy/KvZyFrp2GvtYBdRv0XvEH/DFmyY/71YzRH30WU67A7RKAzK7Zazbh5XWpzz2fPU1oj0TvAkfSCBirzY6nMjyUtZhVXPvs2gX1D223jpzVplMt2RJnjXmRkJ8Zg+/VuetszGAzUygZ2+93ImNvfRWymLQjrN4z9Ez5g6M47SH1vDg5zXiV88CQ+mjeACpOVffFFzH5rOxpnDV5eBgozawgOd2XLLaNI/OK2E0oeQBvc+el9ZyFLyqs0L/lpSo7qUeFMvfoIdm2tct2VkLLzMZAFHBv7IQhaJBppNKSAGsb2W4vLnEiyE9KJ27qPoxUZ0JSLIKkIcu+Lk4sjGbnJFNdlAqA+haZArVFzyeNXkfx6LNqDpRw+kEbEY5MwuDuzwN3IsfQiBn2wA1dHLc4qkUAvRz6ZO4DZP8bSVNZESB+3Dpm3xge6seOJKUx5ZweSJCNbJCxqB+6YfdE56dM/I7ekiv1b16GS4epLOn7OP7bPR2M/ipfGgXLbSPSec+h3ju6J0ND7qTq0l9TUF9pV9Ik5B4lLeA2QuXHOL9gliZ+3PoevuJiAUyw/TTYHgrxCW2xD0IgsdrUQ3CTwzePjMZxDzvm20BGvGy/A2qzk9cB04A1BEPyabfAAC4CjLdQVgK+AJFmW/9uNcncItgrFHqf2+OsVvXnZ+zgcex6rKgLmfINm6ERsmUeRapvQhp6Zlqw9JC19l8PJVQyKcGL6y0uQZRmb1cq3/3qG2tJETPWN7bZhs0l8FZuFt1DFvR4JFOwppTInkYHZ32IR1Bzwvhx1/0voO2YO41pIXzhy+kLimyqIjHsep98uof43A4XqQP7hMpwiv7n4BfpzKL2CwtxawiLceHeEA0feupihzS84Cxryw68ldOqtnb7+zsLHcRpJVV+ALhVBCqZedwS11UiU8U08e0+lNHUTtVUJINioEvciokHEASfTILxd5+LSnI4vdEBvQgf0xvXDGBKTS5g0KZLBC5VjkjSb1IQsLCYbA0edNA8l7zlK/Nr11BTnEaEOoK/raAo2HiTiqokMMBrQHKvGApSWmygFMjKr6RdTAEBEH3c+v3xwh68z2Ggg7dmZ1JpsTHxlLd7OeoLdXLupF9vGN7/+gUq2MXnelQyJ6hh9syRZ0dgV1TFgyHpCvLp/FH8qXF1HIAgqBKFt1We1WUhI/CeBBoWptd5UT0ZREr6isrpSbXbHVVdJpckb2XApOk3LVoNnVyZSJUu8dtswjH+RkoeOjej9gEXNdnoR+EWW5dWCIHwvCMJgFNNNNnAngCAI/sCXsizPBsYBNwAJgiAcam7vDDfMcwVbebOibyUP45pdy/ki9Wt8Nd78a+KjRIR0XuF2BLLNipi6Apvgh/rJ/QjNrlXq8Lb9zFtD5bG9rF22ER8nG5Of/BxQfKY1Wi3+ffpRW5qIwbV9f3S7XeJb3eNEirnQCOyAXkCcfjT+N3zB0ID210qGzbuL8lGXcuSnR/CvPYRd0DCzYjHjhN9J0C9EN2QUGvdgivf8QPTyxagFxcyU4zwM39t/Idzl3PN+FCRks2XNTgosl3LH5TfjG92L+oosNGojOqPi9hY0YiHQcYrkcVcPIOXpBnIPVTC4uZooikQNOum2J0syK976iqwDKwEJldaNCk/Fv9pSqlgwv1utcOb8a3AgwUN9GR/kxnVLD5JVVEeIrxPrbxzVoYXUvRnlfLA5nUhfZxbtzUbJsqehvr5FS+k5QWNtFXoBDh9LxdnZkSG9g9rNNCWKGiptQ3BXH6SqvuqcK/qGhhRk2Y6H+6Q2y2UWp+DlcDI/wLYdo5HR4Ng83hk54leCPNt2oLBLMmuOFBHl68yMfp1zkuhudMTr5gjKIuqf97c475FluRCY3fx7F5zDdDPtwJpfD6KAyuXk2zYx5RB70naS35jPr01r8cCVQlsJj255lBU3r+pQu9m56WSUpDF52ExU7SQMkfPiEL6ahhZoUHlgEM5++SFn7wZAYOT0KagNpy80hwwcQPKupRzasBP/iLa513U6NU5DroHDJ33C0/s+T+TQi3DugJI/Dk9vXzwf+uHEdsze7QjbXmV0wSLEwm8BGAgc9Z6LS1MBgfWHCHhg/TlPci5JEis+WkJCRSoqWWRK5ER8oxVF7OTRtYQhx6E1OqATBfIqTBTElRAw3Ieq4mqcPRxRazSk7jvKjp9+oabkAM5efZlz3934R4aRtHgjHAEHbyPx6eVsrFLWDO5ZeNK3+o8bR3VIhvc3pfHpjgxMVmVxRZJhT6biXtnbXUt6pQU3semsrrMzuO2q+Xy+eAVkHmJN5iGWuobx5kM3tVuv3qLDXQ12e32Lx7Nqi1hdkMLSsibMkp1rvB14qF/X1nQqK5W1CxeXtqNiQ316k3TUSJPkjpkgtHIhNllDLaGEBV7SrpIHRdG7GTTkVDQSm13JiNCO8V+dC/w9o4i6Cf/J/YDNvWPo910foo19sVqtLGn4DZt4km/97qBbKa4u5svGH4k7uJthg9tO61VVW8E1m66jXtXI9dn7eWzhc23K8OOaPI57bjvaK9j7yV2MubdrwVEAmeu+ZcuGWDwczARPv+6M43pnZSHRbuuYp0XAgidhwZMUv3sDvtUr6Z30PI2J7yI/n9lu1qvWMGrMJBgziZzEGGpLsxFMtfj37k//iDEkvzoJC9rzcuMVJOeQUJGKm+jEP+64BaNv9z1oKo2Iu5uKgpI8tv2STe03P2OqEZHtzqg0BmzmckCNV+hornvlcVRqNeVHMnE+rMMqWAieO47l6076mHclgOaT7Rk0WU+uoF83Khg3g5brRwfja9Tz2GsfIJ2SjPpcIyI0gNcevYdHX3wTF8FEkG/7iQVSqhpYy61Y5LGU5+Zxs72Bmb698HcOZmnmdlaX1bHZFAK4Aq64Uss7JVou8c8m3DW00zJabcoMpzx9A/n7PkFndSM0+iG8h52eo0Cn0VMiXUpvh0WMG/sDDg5tEw62BK1aZMkdY7jz+zju+C6OPY9PQ689G/+6ruN8x1GcVxzzyEYnaym0F/FN7WJ+aFpGGIEsm7yEuCv288Pwb1g49UYuHjEPJ7uBm4/cxe/bfm6zzR+3f0u9qpHe1mAWN/xKYtLBVsvKkszr2Vqmm3+g6d9lxHhdwZiyX9i97IMuX1NunOLPfdHtd+Hgdeaows1PUWY1RcUkfXaEov3FHWrX596vKHS/knp8MIjVlG1a2mUZjyMkehRRI2ZBtQ95O7MpjtlCiCWJMuPATo3mC7JLST+a1+nzu/p5IMgCvbxDulXJH4dWl45k/R593/foc3k6UQszcfa3otYa8ekbSL/LLIy5LgKrtZJDu97jWPKnCIJIo0sjGkcHHrq0H07Ng4rYI0XtnO10PL8ykSar4ped+eoskl68iFcWDOCRiyLxNSqmShUSNkTOZ86JmKQcXAQlW9XwAS1TQxzHrqJqJh1IZaejOwdsI6jEm6cLPRl5oIag7Yd5OM+dPU2e+FLKk145pI3txe9DwpARuPxQGsuz9pBXX069tQmTtQmTzdSufEGBygyjVpWITWelLrCIhJrHqMs7fEZZo7Niyt1y8LdO9sJJRPo68/bCQVQ1Wnl3U2r7Fc4RLugRfZm2ipkRM3lmzDNYLRZElQrVKckuBkUrwT6RodGsu2Ids5bNIq4kjku5utU26xprEWWBt6f/h+u23sh9ex5kS9T2FmcBgigQ7aDjsMlMRnYVg25+l2PvpTHu6NMcSFmBdcA1hA+dglezH3p7KNm/jvijpRg0dnxGzm6xjKWkktFe8wihH2TVUF1vxW9k+6MRQaPF/4EvaMhJhW9G0JR+EGa23g9tIXfjRrLiC1CpVWTnaqmxeAFOqGObmG4cguf1SmRpbXUDqUeyCOsThJuXM3s3JFCaX01RUgMaA6h0AsOm92LPijSkWj1xA1O5+p6OJyDXqDWoEKlt6HzKv44gNWUZA/6RjqiSkerCURuyCByfR3jItWTmPYvW2UJu+ZPkHg+A9QST80AEDyUzlVat4o3JEdy7NZWXlx3l94Ht59o9jnVHlRdDuKcjoiii17YwZjPXYsTOs8+/iHP4EB69qWPRoF3FF68/ToHppOPDmg2bGRPdujvhpuJqEAXG2tQsnzkISbazPCeOnPoqllbIWNGyftRgvA0n13Eidc68F5rNC9lwb7YBsk+6sepp5MsILdMCW8y9QW1WLIm77lFCNoG+Qc9jq60gRXiXxN13MXTWSrTGk7OQYd7O5BZAVOLzHN6ymHKXaAwGA5KTLwOmX4+Lx+nPlcUmUVTTRIjH6dxCw0PdGRzkSmLh+Vsv+TMuWEXfaG2k2lyNn5Py8Gi0ba94u7q4E0EYm5p28I/8FHoHthxUMypsDD8l/cqifV+z0Gk+3zYu4aXlTzE/+jIG9T3zBnvnlhFM/3gXX65L5d1HJhD5yEZifniGIdlfoj0Qj+2ASP71OwlsJWes3dTIwS+epjw3i8RchaAt2M8JUXv6ArPdZiP50/U45RnwP4XHw7mskcRXY4j893DUmvanjXaLhCwLaKs6H7wsSxKJ3y9hx14vZAIBES99AdNnNuEZFc4fH+/lj+rH6Jsg4luTyZav0xDsGmIo/VNLDphMNgRJxa5F+dAcZVCaYOeuxXfx6TWftipDcWYBtgYL/tEh5CZlYRPsRPbpXIBUR9FnTiGiSobGQGbM38juPx7AZFxDfvXjaJ3BVjoMJ7cA6pviUbsoXjQ5Y55jROTOE23MuSiCI9nVfJFVSklRHT5+7S+i51Y2UlKrmGReubR1llDvPkOpKCvFXFOOkHmQI1kjGNiBxO1dgaW27DQlD2CsSWXrN88w9LJ/YTS6nlFnpq8rn1ZWcdhqZnl2GdN83bgyTPG9f0SWscugbiFBy4KwCcwIaOCXrL3UWhuptdkxSSJrah25Ls2Aa9p2ZhgKWBgUyVDPSJy0TsiynYOHb8DubsOvciqBw+7AJWwEsixT9tMGKv2OsXvbWAYE/wfPIZdgayyjIvtfiKKMqb4PLnId0RVLUFcoprI9pmrG3vTyaXJ9vTuL19clM2egH7eODyPa3wWd+mR6Rav9LGhOzhIXbCrBzJpM5v82n9cmvMbc8LkdqpObk8m1mxWL+nD1QF6+4k2WbvmBI5UJPDbraTJyUliZ9Btrpa281+cNvNx8eWbPM2SochFlgV0Ld+HseGYU7g0vbyGmvonND08iyEeJViwpqyB56XNMKv0es6ym4v40/D3PNC8kfvcyf6zZB0CfADUajZrRN/0T134nyasqj+VQ8H08brIXVQ7lhN81CbtZRXl8GbZ9hRhEAed7BmMM7hgzZMkrMzFajqF9JguxlbR1AJLNhqkoh4M/bqCg1AWLVU2N1YtA1zxmPTkfJAmt68lramww8dXTGxCblD6Q1RaiprhTXlBDY7Udo7cev3A3jG7O9BseSlpCLhs+TkNotjBu7fUTKd4xbJ+9hd3Lt5JTVoCXkzv9ovuReOwYZbUVVNgVv3gHtBjVjpTYqpjWbxwTrpzRoWvvKMoT93K45Hq0Zj/GTtuESuuAJElkHVtNQcYOZFUtY2a8g1anjO7qqgvZf0AhbnNuHM7IuSdNhKnJZVz87X5uCvbkuXvaXoh9fV0Sn23PPMFB8vu9YxkU1HYyi/Uxiexeu5QGlRN33HwTkUFnn5S3qaqImJVfYzZbcHZyZEOq4s6rxooN5Z7RYOUpPgTgkG4YksYJt7kvEBZ10rfj4d1pLLYoPEsak53Yif3xdexagGNadS6fZxwgpdHOfpsy2PGRivlZOkKTaT+VjgcJqJhJ1MJPzqhbuOcrkkyvIjaCSi1j1SovmF6qsYROaqZAsNuoKMnH4/NBbA9/hEk3PnNaG7d/F8fGYyXo1CJmm4RaFAjzdCTU05GNxxRq5+zX53Tp2jqC/8lUgsX1im3az7HjI5jgkHD+M/h1Pjj8IZvl3VT+fAcHxUQANq29+ES5GaoJTB2jmE6+D/yJWT/Pok40s2V3CfNnnqnoH5zRhytXHObBz/ax7OmpiKKIt6c75lKFJjVdDKPPn3ydrdXFlBzaxo4Nu3DXw02f/4aoPTMeIP277egSBZxkF0yD7PS/9tITZiSXECNJ+4uQZJny8gqcAx07FHhji7wch6Mx5K78idIigaNJzjhozPTu1Yj/wDBcI/qQsyOW+F1maqxeQCQ6sQE/tzKG9VcRdfnVCNozH1aDowML/zWGZW/Ggk3N1Fv60G9o61P7PgNDyF5YQNpSE1XRqaQ67wfgkW8fI7ghGD+NB5k1eSTtyTpRJ9jBhz6hvcktyie3tgi1rKLPkO51m7XbzKRkPAtOEOb9IKrm/0UURXr1v4Re/c80kTi7+jMkbCkHsxbCnwjs+kR5EaRSkV7ZNrFcakkdn27PRKMSeHJ2X24e1zHPoYtGRVNWWUXKvs1889XnTJt9KTNGRnfwas+E3WLmjfc+a94SUHxzob9LHZc9+CbmugpiV31J1IhZHMrqheXYGvrW7cHZ3AhLJrPdZS6T/vkjAO+Mi0C9L50famuxOqjYXljNVS3QPHQEEa7BvDUsGEmy80XKejYWCuwS/YgVXsHfsRBdmSO9Zr/SYl3/sbeS/8Ab1F1qR2p2FAxxnUvo0FPIdlVqMmPX4gGMzPyQw6s9ET17Ez1iKqJKhV6jwlGr4o+HJnIkv4ZjRTWkFNeRW6H0z+TIbs563wlcsIq+qEGxYXZG0QOMGjqRUUMn8tO6r/lv8UdKGzYvQjVBTA+ezrBeowgL6M09q//L4fJYam25oKnD3hjCg1tSCQswMjD69OxKw0YGMG9nFr+X15KbU0NomBvIMsEoL6OmkCloVCKVhzZTV1WF3FDBqh9XYJHUuGhlZt1+Z4tKPn/nIbSJMpWqUkLuHIdriP8ZZayOApssh8lduYVhyeOZd+3pbmk1VfXYrFaM7s5Ul9VxJC4VX/+RFO67krgNfkio8XYsotGiJ+aoNxyVgRTABSdVOUOj8ggZ3gu/sZNbzGP6Z/gGenHLq1MwNVlw92o/rL+4qhxwYo3uJ+Rm19RY71gGVEVw50P3YzVbif1jN87OzvQaGomDi+HEy0yWZWRZ7nbStL3bZ2B2KiBM+ziBwzrue29pUIz1AQHXnHGswi4R1Y7nzYKPdgPw8PQ+HVbyx3H9rPEcCA/k58WL2b5mGVq7iUljhnWqjZM40wpw7Wh/ImbehiCK6F29mXjDkwB4R42CWQozyuGtvzBo++1Mql1NwutTsakMiJKZawdeS1DQDF4tK2dJTlmXFf1xiKKKO6JmsW3vUjSBHvQpG4BK0jH4suWoHVu/5zxj7RhjBCJ2HEDUtcyPFTn+MuJS1jCgIYZBcY8DsC/5HmRLPTfm7yNC6kdNwwjmDPRjTifWXM41LmhFLwoiXoauvUWvnXULCxqvoqAkj7DgiNMWcX+Ii2NH+SIEQUKS3THn3YCtXvEwuPT7WOZ7uvDK3aMwOCrrAstXJvN7ubIQU1Oj2FYtFhM6wGxXIabv48ubZlFjOtWGribcX8/0f72Dc+CZibLN1fU0ripERCTqkVno3E/PcCTZJY78EcMWaQ+1KmVEcTB5P/OYTl5mMWt+W09dQw0NtmoQTh9hOtX2Qt94Df7GAqbfNQbnsKlIkkx5ai6NWclUZJfh1TeMoAmXI6g67y5mcNJjcOoYu099jQlwwqo63R88zniApLQjmCQTY+dPbrGuIAjdztooyzJmQbG3h427rVN1rVaF0E3XwuBjvJsj66vqid2fz4iRpwcNZZbVY7VLNFgU+/DtE7rGl+KqVSEWFaD2dmXrut8JdjcQFtnx2Y4sSaz76lXSC8oANwZ7WBg1aQY+/Sa0mbP4OAZNuZKm4TNJ+OZ+vKoPoZJteMiVOMbsJz73OuhzB4ct3eMO+uL3y9ga1od7q4sYdd3HHapjrlKe14p/X4fX+y3H1Lh4+DL80TXUVJVxdN1HDEt9h9HZSvsZqhDuF39DjtFAwGfQRffkc4ELWtF76b3QiF0PytEbHOkddrqLmCzLvLz3bTQuEs5Sfy7rewXTR4yjb5QnBQW13L9kK6ttMax6qYoXR/Si0WLn5cMnXQMtzX7POgcDi/xep2LLauTmqWKYm5mBkyZSW1lN9NX/Qudx5ggdlECgjPe34CS4IM52RefuRGlGId//8D1hnkE4OjqSmptBhVSLAR3XTL+c1Tu2U2cp58VnX0ESrSALGERXgr0icHYyUltVB6KMzuxBTbEKBy8785699sQCrigKeEeFQFQIoV3u0c6jqc6KoG5k/VV/ELtlO1+Uf0uqKosm2cR1u27CKtoYvWcIn974zWkv43MJvT2cJlUmJSlr8Y3quM21smQXaMFmOjMw6LGrBvLHp3s4klF5mqK32iSm/mf7ie0RIW5oOpBy8s+IiT/Kpv++iItkwyE0hBpzA5tXr+K2Dir6vNh1bN66lexGA66IXNJXz+DLn2pzDacl6J1dGfnASdpfm9XK4q9u4ek+dwDQq5PttYTVu2P5JCiCiQXZ/POyjnPuBD55M/mvfkNDQjbtDQ+Nbl4Mu/Z5Dv8RTMTex8ma8jHRk6+Anf9F2PwCIMPoeyCwq7Om7sUFq+iLG4o7bbbpCARBYF74fFbnNFLneJQf0gp55ObLAQgJdaXQ5yscNHmYBDtPxiqKp59aw0gvJzw8DIwYEXCircKcUrQI+LiLzHvsdYwhfWkvqWRdfiklHx/ERXKjyr+KAZMmA1BTXkWd3MSRslQoA2/RlYuHTmH4rHGoNWo8woNY/P0yRJWIu9GDCdNGERh+ptvlDy/uBKxET/LrkJfOuYatHtCZ8HDy4OJLLmOGbT6XLppDmjr7RJl94kHsVut5UfSCIDCo/9fsS5pMUfbKTil6SVC8plKKn8B30MnFYckukf2H4nyw6Eg6N17eF41WUXif78w4UW5kqBuf39iy62BbKCmvYv27ryIIIjMefYURg6N444XnKKyq6VB9q9nEV2tiAIVH/b5/P4vase0F4I5i+6H1/DviQUabc7mj72hmBJ4dJcazy9bwuUcAQRWlfLdgJg6GjvPCOl5+L7z2Nfo+Hc8NMejiW7BPv4Ho4y+oCf8EaxPseBOOLoPLv4IBV7TdyHnABavoixqKiPbo+oJTW3hrzjW8xTWM//Za6qUSZFlGEASWJ+3GplFG7zqf1dzlPxc3B2duWtAPVQujsGvvvYtvnkinqc6K1cm3XSUvWezkfxKD3uZIlW8l/e5WFoTrymvYs0lx2VsweQ69B0RicHNGOMU1zdPflfsfa9vU0FRVhX/Wz9ToLiP1t3RGT+8aF093QpBE7KdEMqvUKq4JWMhrJe8yX3sR5dYKdstxbN7/B7MmXnpeZHL0C8IlbjSVjpuozj2Ma3Db4fTH4WIYSKV1I6L95EK1LMscfet3Iqo9mW03sVal48tVe7j78kl8vDWdt9YrQTbbH5lMiGfnc78CfPv+J+gtdYy8/zlGNi9M+3p6kl1eSU5aCiERp7ufNpVmU5kag84rFI+IEag0J12TI50bu03Jxx7ZyG01nvS1lfLdpGm46Lt2faD047vrtvK5RwBujfV8Fx2Kg2PnEnwIWg2CAPaaWmS7vcNmSdWfZyGTHoWYT8FcC/lxfwtFf0FGxkqydM5G9KdimPdo7OpS/rt7JQCHChXvD53kjyBIbFX/wC0L+7eo5AFCAnzQhkWjt9ZzpAMp11I/2Yiz3ZWG/mYG/HM+Kq2G0uwi3v/gA7KsxfR1C2fQ5BE4ericpuQ7AlN9PYmz5+C3dzNeZQeptzqSt25vp9o4F9AYwaHRBYv9JKXDtRffys7523jp6rd4ZbbC0/N7zu/nVa7AYMUNNz79MmpyzyBubREqjbKg7u98PVlr91GekkXSh+twr/akzqGG/z43FT97Je/vr+C+H2J5c71CeDYs2LXLSh6goaoCq0rH2NGDT+wbNnIUSHaW/vTTGeW//uwDvtiUxIeL1/HCiy/y4osvKnXcG7j6oVe7LMepSErZzfUlDvjZa/lp3JizUvJJKWlErdnFG3p3QstLWDOsD3379ul0O4JGi2OEKzWHKii8ZmKX5SFnj6LkAcY90PV2uhEXpKKvNFVilaz4Onaen6IzeHz8TWB34tuMpxn+zWxWFb0FwCS/+UTqFpBj2cEzG79vtf4XX/yILnkX1Y5+TBzb9pQ8f/sh9IU6KjTF9LtBcfVM35/EJ998hhUbCyfP56oHb+zSdTSUlnLg5ltwrKqChx5kzie3obE3seWXbCzVf100H0BohA9auwN/7N552n5XVw8EQcBmVcwhsv38xoP4DLgIY8M4ABqqstopDRZzA9nln6Jp9Eb9Ry80O6yYvsnHpcCZan0FfR6/CAcnR164JJomQcfqo6U46VTEPDGN5feMOytZ9e5e6Owm3n7pPyf2DRg5Ck+9jnpZICEu9gRNQl7MKsrszuhFGwHaOlxEZSHfTaxn0uW3I6jO3giQk3WAq7NN6GUbS0YOxsv57GYInx5OpcbRGWNjA6unjyLc17v9Sq0g8JftqA0SlpKzuO8DhkFA8/N8bGXX2+lGXJCmm+KGzvvQdwV+LkYWz/6Z69fcjlmdh8EeRZTrYG4ZMo9wN2+m/HiUFfnvMjo5mjlRpydSOJacSe2mxZjVeq598lnU6taniblr4pB31GOmifC7JtNU18CSz34gt6EYBJgQNoLoyWcQjHYIsiyTcPMtuGVk0Dh3DkPvvBNBEJgwx4fN6xvY9PxvzH635RdIXUE9ZSlViqvoBH/ULaQhPFvMmjaej9duIGVvDbQwyMqrzAEg31bIL5u/Z1T0OEJ8z30WH1EUiRz+NLEJCyjM+xn/QUryGJvVzL5Nj9IgrUett2IuD0AQQO1ShGiQ0CdcR7W9Bo2/DXu1GdzU9LtrLiqN0nczxg/BYV0BJklkhGM9Ho6d69Nflq8ja9nnWMOH8/QrTwHw8JP/5KMbd6FN3sny34Zw+aUzARg3cRKr1v3B8tVrWLd6Fff+6xE2bd2BDjUPPvgIDkaPE+3KktQh99n2UFqQxFXJJZjVzvw2MJhgt64rZYB9BxNY5u6LaLfzY5ArnmfLvS+KSHYBu1nu2jXLMjRVwYCFUBAHhr+OsfJUXJCK/oQPvdO592Pt7xvM1muXc7Awk8lh/U/z2X5z8ks8tftKqnOu5MP0SVw54U28mx+ezFwl/Zxzn0H07d364k9xfBLS9lrqqSb4wXE4B3hx5I/95DYU09spkEtvvhInj67nxD382msYMzJouPpqhj9/kokzasEo8mKWkFodyMrH1uPi5YnVasdqkbBZ7NQ12Kg2nWRO9FibzYLnRqFz6VpUY2tw0GkxG2uhuuUHbli/MTxa+hAfZn/KS/lvosoTebHPM1wy9tzbRZ39+qCPD8MqVJ/YF7/zBcy61Ug1zqC3ovMswNqkwloViqt7NCPuf75Nv35BENj3xFQufnklWytduOyFxfz81JXoHdrv18rKKvJ++Qg1oE7fywdvf0hhdjYacx1GQEJgxIjBJ8oPGTcB74AA1q34lfzqOt5+6y1k0ZFoMe80JQ90i5KvKMvi6kOplOh8WBbpTpTP2eWGlmWZe7JK8RIEVg0KJyg89KxlFDQaPKb3p2xNIrb8TDTBHeOhAiBjC/xy00mzTe/pisL/G+DCVPT1XQuW6irc9E5M7TXwjP0TQvtxd5qEj1rGV7ONvfumYPR7lckD5+IfruGYv0hFSevsksWxyTQuLQBkQh+ciFOA4pGQnZMDMlx6y5U4uXdNyUuSxIGnn8bx1xXU9OrF8CefOKNMhCmD/EaZPHwQa2pQCQr3iFoloNepGNTLiF8/d4qSqjh8tIKcHQX0mdv9o2lRJ6Muc2rxmCAI3DDlVhaariEtM4lX97zKU2kvcCg3nutH3Ux4WOdttR1F2safaTQkAVCan4CHXyRVtVuRBT0XzT+AIAgUZe3D1TMSg0vHR3auzo7see0qnvtsJd/nuPPsx7/y1j/PDLL6Mz547iWcAGnEXITY1Vhi/+C4D4sdkWEPvUxwwOkj6IDQcG57+BFW/fQ9CceSkOqqKSgvoyE/BcdW+J66gorKIq6MTSDDwY/vQhwYFnT2/0uDXaLQzYP7clMJCp/aDVIqkBqVmA2Vezum3/I0WH6bMoJ3cIHihJPH1A6wcFG7DhbnCxemom8oQq/W46Lt+ki3O3C0YCU+agtFmkEMCXqMo8f+ha3geWJ0DtTm38WweTIVyflIknTGKK9w91FqfsvEUXTBcFPICSV/LC6BA0WJRDqFdFnJN1VUcOjWW3FNTqGmf3+Gff8d6j+RvjXEJ9P0xw+M7zee4HffOSMg61To3HQcPlpBc1qjbkX8saM4Fvpgjy5rs5yDg4EB/YbxsfsnPL/+GZaaV7Nu6xbWuqzBzaP7s1jJsoxtt/WEOSkh9VJIBY0LUD36xP/pHz6mS+2LoshLd1/KoeeW8GuxgSeqa3F3bfn/ttnsLP11LU6lqZhUDjz1yF00NdxIcXkV/v7e7Nodh7e7kQED+7V6vnnX3sA8IH3V5/z+Qxa5O1fS95p/d0n2P6OitpwrY+JJ1/myKFBgUu8zB0VdgaNKxKmpkQJT93Luy3KzcpZtrReqLYIvp4GpBkInQHUO9JsPk5+ExnLwigJd68/M+cYFuRh73OOmu6MiOwpZllmy6zKKU/5JtV3FuOiXiQ4dRVDoc7jqqqgvuBOxOZzfI6qG5PgfTqtvrmvE8nsJzio3GnqZ8ewXdqLdX1YvB6CosYztP63H3Ng+B/efcfiVVzEmp9C4cCEjlixGoz/T19hepURxul8yr00lD4rLI0BNeedlaQ/52eUIiAwcF9qh8m6+3rx302c8H/4k9apGth/a1O0yAWRs2IuzKQSf9P8S4Poc1jo3rHVOWKo9COnV/ui7o7h6dCh2Uc3Q13fy5S8bWyyz9Ne1FC5XuGd0fRVSNL2jgbCQAHQaDdMmj2lTyZ+KgBEzUAt2sg6dHangcdQ11XPlnv1kaLxY5GNicuSIbmkXlNnckOSjrAmNZNnn35xVW7Ik0XTkCGXvv0/lNiV2oen1i8BySu5lyQ65+8BuhfVPKEq+7yXwj9XwUAJc+R14R0HoeHA89ykyO4MLdkR/vsw2LcEu2XAyHUEUYejQXwhzVx6ysf1msK7+ccrKd+DpGIHOvggAd+/TRzjpy7ZjFJ2oU1fT9455J/YLgsC8GbM5tj+B4poytqbuJeG/x7jniQcRO5ihqCgxEXHvXprc3Rn20outljtOSiaZ2k8y7hPtjodeRUJ8KUOvi0Sl677bauiAvuSvTCQ1NZcpQzs+Og50UgLT4krjuUS6slv5bix1TUibqzEJIpHXXITG2YGooV3zeGoPV88cSVXler4/UsObcTpcXfdxxczRHEzMZOOvK7A2NSBlJ2AAXKZfw/U3dH1t4tCnj1FTUYFNVpGWU6ssLJ7FYGl7Sjx35zRQqfPnR5ciJvef1eW2WsM7l0xjZHY1D/QahGbDVuZOnYCqA3QMAPb6ehp276F+2zbqd+zAXlEBoogusg8OzrUYVAcg9suTLpKHfoKV951swBgEV7XuVfd3wgWn6GVZxnL4KFLYuUn03RHsy/wKFTJleDDNY/Bpx2aNvB24HUmys2HV7yA2YvQIVihuV+yh6VAZrlZP6oUaXC8+0949bNxIho0bCcCW79ewIyOWnz/4jvn/uAKDa+sj74T3P8C8bBkO5eU4yjLce2+b19AYp7B2Goa1H3QmqkQGTQtiy+psUtdl0/fSTixgtYPAQG/Muv1UZ3Yu9+mIQeOZcXg8v1v+wGulJ9eOuwkvr+5xt03/disuojP26Xo0zmeSzXUnVCqR+66bxZwp5Sx8bzOPbypm6+a3Ccjdi1qyIqt0SI7u9FlwDfNmT+7SOWSbhYNfPMPWbYkn9hn1zV4nZxFt/ERWObKo5xlVFtOGLehyO20hsHcvfqw8yAPFtdzp7Eb4is1snjsRfQuzVABLTo6i2LdvpyE2DqxWRBcXnCZMwGnyJBzHj0ft5gaHf4YVdygj+OOKPvt0F1+uWXJOrulc4IJT9EWVubzyvR04ysqdc7nkk9WdbqOyOAetVo+Te+dcvxrMFayNvxfXplgaZDVjB7WeIEMUVQT530Vx3eukJyyBXQG4VbgjS3pqAmuIuvviEy53rWHCwhlUfVbF0cp0fv9mGdc8/I8zyuxYu5pja9YyafNWTG5uNE6cQOTDD2OMbHuhTW6esgoOHQtk6XNRCHvX5ZC0r7hbFb0gCEjeDWhKjEiyhCh0bGQuiiJvXv8B1yy6gi9rfuDLtT/wZfRHjBresUAYWZbJWLIDU0kNUXddhLrZ66W+qAJ9no4qhzIGTL+sy9fVWYT5e7LpiYt5+Im3Ca88iFXryJUvvkNo2Nl5rmR/citb9mRRZTHgZ5SY+/gbuIS3nsyko7BLEpk6H0Y0ZnDvzMvPur22MGXkEOItVoJ3J5Lp6YPtlAQfstVKY/yBE8rdkqXEPGh798LjphtxmjQJ/ZAhCH+eBaSuU74HXgn7vwBLAxQeAq0zPJoJ6rYTGf3dcMEpes86meMMHhFbM9jx3RtMvPGxDtffv/ortE+8TaNBhemWywgcPonAgL4YfFsmGDsVq3dOx1OsJV8IZs7YH/F0bLuOxaS4YdUerCKkYgBVrhX0e2Ruh6eeGgctC+67lqyX36SuhXR5DbW1bNu7j7m7lQhX3cD+9P/44w6ZMQzDoqn6FhpjEjAMaN+TRqVR4W7UUltv7ZDs7UGSJDbu2c3BPZkY8v1oMnQ+gEWtVvPNVd/z3zWvsdS0ip25O9pV9ObKOtK/245casVV8sQBN1Je+4PQeyZQk1GoLJALRrwvO//0EK6uRoZZMzADU69YeNZKHiDj8EGqLP6MHRvFqHtf6zRJWWtYeWAj4MOdrucnq5KITK/yEjI8fdBbzFT/tpH6bdtp2LULqb4eQaPBMGoUbtddh9PkSWgDA9tusOCA8r30ppP7BBGu+Pr/nZKHC1DRW4sU10rdQ3eS/csi/F79li25uUx9+qN261osTTS8+R7OViWVgt87S7GzlFQ1WN97mhHTrmu1rt1uxVOspVhy4abpW9s9V0H6bkprP0e2a/HJiqBeXUP0v+chdnKqHLd6N/WyiRG9Tw/IkiSJrz58H0lUUfXw/ehfewvN9p0kvf4q0U8+3W67Dv2UUbklJ6fDsqhkGbNdRpbkDlEwfL14BaVHzExYGMm4oUOwS3Z2HoolLiYFa4YDLvVeOIje2CMquPkfMzs8mj8VTk7OPHn5i+z4bg+L6n7mH5V34NnKTK0iPpPqX9JxxhkTjVT7VSNrwJjrStU7illDgw6mOOEz+Ny5bR5HZWoeFQczsZus9Ll2CkWZKZjrajG4ujJs7qXdcg7jgGmwI4mClGQqj+3FNXIUat3Zx0JsKi1HrzUyZ/S5Mdmcirr6Bmb+tpmsgGBmxOwk7Z5PQJZRe3nhMutinCZPxnH0aETHTtAszP8IDi9RlHr/y8G9l6Lonc+OK/+vwgWo6BW/9MDZl+F66QJi7rmG0B+2sH/A54ycf0eLdZrqq9n84NW4H83Ht8ZO9TO3M+yyO4hZ8j7m+mr8P1pF3XNvIE2+ulVFXNGokJnZNAEtHj8V+amxHDl2G2oduB+8G1eVH+IklzPatppqKN23HQd/Pzz6tOyt4BXog3BA4HDaUQYXD2f7ljXk5ubRZDbTIAv08fVi2k23YL36Oo6MG4v0x3rogKJv2K/kjNWGnTny2bV4I5l5WVxywxW4+p30Dw/u40ZuTAlFSZX4R3ucUe84JEnii8XLsOx0xxkje7/JpaC4hMyttTjXeaIjAMmjEpfRDVw+dwYGh7Ozg6vVarxED0x2C2ptyyPW9KU70cbZsdrNiHPd6DN50oljJQdSqdidjmBQETJ/FAZP17OSpzVY6prI+noHdrMVSZZwrXJDjwjoKHxmDz9nKbw+wZGDUHUDFQFA1Pw72b/3AXIqRBa98iYOKjvR/YOZ8O8PTiMz6yhqrDYidx0FfTQLbRnIwkjOhe9bo11iV1UdmypqWZVXQlVAMLevWMy1eal43ncvTpMm49Cvb9cDvcImKJ8LBBegolciTtW+vrhrtUxatIYjU8ZT+uN30IKiL0g5QNYt/6BXhZX8ID0N/f2ZtvA+RK2WiTcrWXKWx8fRb18RjdVlOHm0vKDn5RTWnMy47alvfVUl+3ffhjHEgljSG3fZmyq/agZcfOZNlfvxWnSlgdQ5FOH27Elfe7vFQlNlHk35ReibzFzs3Y+NpUm88+n7SkW7HRUyfQL8ufr2OwHQ6HTI0X0x7D9AU3k5es/W3b9kSabiy0Wg0mK8ZCpNmzZhOlaC1k+HrcrM5oI0ZAE++PRDbr3mH/hHKSYEr2h3iCmhKrUK/2gP7HY7B5OTOHIkjaYGC4GhXowZOohFX/yBIcuPpoASxs2JIv4LG2Ur1Yh6NW4Xmbh48njc3bovBkK2SWRJeYRLQRgdXc84Xp1VgBhrpk6oJ+CBkbgGn25y8xnaB5+h534En/XrbhyLFMZFq2QGEcqcinDwcsHeYGUwMziUtZG6jKJuO6dTYG/uXrSK8j0/U5qZRsbBeOIPF+Dw8ZOMfvDtTrdXaj5puluq7sWh9X/w04AwggLP3jkiz2RhU0Utm8pr2V1dh0mScVSJTHRzYm5ZAfOfeQS111+Xru/vjAtQ0Reh8vJEbA4AMji70WRQE3GkgoQdv1K+ZweaAH9qczJwCg6ncuM6IiuslD12AzOaFfufYahQcnnGXDmLkFfeoPdohSuktC6TLYceQhZ1CJZivATQObRtN3Vyc8fenElK8kmnVLuMMZcvO6NcZcJBdKWB2FVNqE1ulOzbhLWgFluGjLrO7RSqWy0B+HKxoGOj9ggGq8RdzzyKTn8mRavvwquoi4nn6G23MGzZr61mBar46nesGfsxXnkrGk9XSjbpgGAaCkFCQnZIw0mw0yDDzvXbWNjnemxmK++t/pJgxrFtfS4rD8SgqzKisxkAIzIS+XGwdNlh9PggjCjjkX9cxY3rb6Q2QmaA4xCevuk+9Gc5em8Jglpknn4mSyy/s/PQZiYOOZlOsSo9n+JPD+CocsH7+gFnKPnzBUttA9pEAbtgx3BNIP4Dw6nJLMAveAyqZg6h/kDjU/Wkpu/FVFWHg1vHkr23B0GlwmvCtXhNgH7X21nz78vZvScJ94D36HPFg51qK8JJT8aEAcSl7mNHfjZfa3rx0qE4Pu+CorfLMvE1DWysqGVjRS3JDUqcRqheyw3+HszwMDLK1RGdKAJRbTf2Pw7hOGvd3wnDhw+X4+K6FrCRe8ut2OvqCFv6y4l96+eNJjit9SQLxT5aJm2Ob1XxleWksP+NRwnfkkr2RdHMek9RzJ9tnUdvWTFxNEgCdYIbc8esxmho245XXZ7Bvh03o3EtQLDrmDI98bTgrtK47Zh+tSFpmpAMJrRVSnsyElbPIvC0ovNzR+ttRG1wwlpbR3VcGlK1RHnkEgKj/0VoZMs+ywfuuQv9lu00DB7A0J+WtLgwmzblUuzVxUTs245Kp6Ps1SWYawMwuKUgett4N6eYaA87tfV+5JlLEWQBWZA5akzCvbYPEfYBSGYZlZeNgD5ujBgSjZ+nF3sOHiTpSC4hfb2YP2k6sUWx3LLhFkJcQlh16apzGuBmampi9k8XoZccGO48BGetM9P9pqJfUYUOPbp5PvhNODf5CzqCysx8Gj/PoiHCTOSt01ssY20y88XtN+Hk4M71n3/Y7blwT5yntpyfHriGeovIwL6+jPnn+23mWm0L965fynKtkgrzKekY8/oMIjSwX6v++dVWG1srFZPMlopaqmx21AKMMjoxw8OF6Z4u9NLr/rJgyL8zBEGIl2W5RRrcC29EX1yMrvfp7n0zft9D4o4VlCcfInTqJdTmZ+ERGEFlUSbm3BzGXXprmzkvvUIioVZJ/+Y+dhJ5DRW8cXgZpWWFhHuAKECF8VpuHtF6ANKpcPXsxbS5a9mxaxBqu9NpN235wRhMv9qxOVfhPNsX82LlRWxxKcHntlE4ek9qsU3vEROI3/EqZlsWeelrW1X0gz/8mAO33ozj3v0cvPsOhn325WnHTRmF2Eqz0AQNp2nVZmyV9SArnhONVb2xjK2EnGL8fbyYecXVLPr8G1QaFf5GHzZpN2Fyb+Dzm15q8dwzxo5jxtiTlLufJSjRnK+Of/WcP7gOej13+93Gy6VvU9m4jUZTEz/ULuUR15sYN3jsX6rkASriM9AjYghtnROn6MAxmqz1DBpx8TlT8gAaF0+m3ngLv3z2I/uPVhBxaAu+47q2qPriqEnExhwmV+vFK2I/Xkm38nDiNzw6/QYEtQZZlkltNLOxvIZNFbXE1jZgl8Fdo2K6pwszPIxMdnfGpQ121x60jwtK0cuyjLWoCKcJp9u7RVFkwOTLYXKzP2/zwmZgn45T+zqMGw1xv7Jh81f8bP4GQbaiURuJlfwYpUpC1RDbKVnrEtMB0NkCqcwppSK5iIa0ZNwLPbE5VeJ/9yQcXL0pqFiDrbYBv0kT0bu1HfAjyM3mKqfWzUeiKDL0q29YPGsqlWX5GFf/Tu+5808cT3noITR2Kw29x1Md5wzoUIlVaB2yEV3N/Lo1CTdRYMTsB9A6ufPAc/8CoLyunGeXv8pY49gO98HR8qPo1XoGenUP90l7WDj7Ri6Xr0cURPILsrl55Q186PkDowZ0PK/ouYCpuh77/lqsgp7g8aNbLeffHLzWVHPucwQETb2GqYl72LIrC5y67mni4erN9ikTaayvpM5u4+29G3jHeRgp2zfiGzSQjRW15JmUpDL9nfQ8EOzDdA8XBrsYUPWM2rsNFxTXjb26GrmpCbVf9yYcSd4fj8qk3OyBJVaCPSdya9/recLfzghVMrmG2Uwd+lm77ZhMtezf/wGVVXlkZCrl6w2HqfpuC/qttbgXetHkmo7PHcNxcFVcAAOmziHk0ivbVfIAPkEKz0lZ+bpWy0iSxC8vPkmxqxMWjZpti76gJkN56aT8/DPq9KNkhYWywjMHvztcCHh+BH6vXoHD9RFstiRQKTkzd8YUtE6njzyf3fAsCDAjYkZLpz0DdslOg7WBYOez9wXvDI67aAYGhPLK5JexqOzcvuZmCks67kba3cj8bjtOKlfEGa6odK0v5qsdtGhVDlgs7dNSdAeCxyv0G6mrvjirdvRaHR5aFaH5O/jAtIN5pVtZK/qzuKiCvo4OvBUZyIEx/dg0IpLHwv0YZnTsUfLdjAtqRG8rVlwrNb7dw3OTfiyZ1BdeIuzwAXyAamcXIl/8jKdZRFPF5wDoIr7m5qCWzSl/RkbGZurq3+XgwXehOamOpn40TVF+qAI9yGxYgCzacDIPxZGwTslaVZZGwuF/o3YGL8+LWywjyzIrXn+egqSjOLl5MGrISLZsXss3jz/AgPAonLftQmU0cmjIYADiYzehcnAiLyuNhGoHJIyMCtTSa8zpCbGv/f1aEuoT8BP8mBHdMUVf1qSwUfq0s55xLjGy7yRernuaJxJf4h/Lr2HJ1Stwdz+/8lQm5KDPd6DSoZSBM9qPINWq9JjN50fRewyeQajHu8QmlDGiLA+9V8eTZiNJUHQQUjdA2nooPAiA4OTLhxFGbvMeysCoCeg7yNHUg7PDBaXorccV/VmM6CW7nY2ffIFt61YCU5Lw1GpJmn4xmqnTmDh9EkYXZ8pq1BypUFKEGR0Uf/G82lw2H3uLSwa/iLtDy6nRoqIuIS//aTQaxXtg+LBdGI0nX0oFu2/CZP6K1LQfCAjoOMtfQWYsR5NvQGO04ixfz7BJj55RpiwniyXPPYalqREndw9u//BrRJUK/2Ej2Pr+2xzKTkUX4I6TxpHpQ7xZl1jN2qQ6oA4dGgb5apgw/ybc/ELPaPuWiFt4OPZhpvlO67DMv6crOV77eXaMVfFcYfbIhdRaankl413e3/Aqz1/93nk7d+L7azAWuiAAQdeP7FglQVDIxs4HBIE+I0aR/UcsSYvfwjt6JL5j5qM2tOLt01StJN9I2wjpG6GhDBAgcARMeRr6zATfgegEgVHn5wp60Ix2Fb0gCA7ADkDXXH6ZLMvPCYLwPHA7cJwo/ElZlte2UP9i4D1ABXwpy/Lr3ST7GbCVlACKD31XIMkyT774Fjf9vIgKNw9yRoxi2MsvMDLgdJc7L2N/Sr0ewbvsbUoT5vOTEISPnEcQsHx/GWOiXyAu4yuG976T3Ip4SitjcHcbR5TgzHFrmZvrPacpeYDRox9l2/avsNnWYjK9goND+3zWdpuFI4fuQutqxdPhYQaNve+MMvVVlXz36P0A9Bo+iln3/etEcJb38JFc9d0vxH37FftXLacCM8cSS3n26ReozE1CsFtwC45CbINbe0rkFBz3OhJX1nFPqU25Cn3wtVHXdrjOuUJygZIwwt35/Ppgy8WKbVp7jR9ufTo2WrbazWi055ZI7VQEjZ2N89YYtu7OhN2ZhP32Mwv+8wuCVq+8cEqTIG2D8sndB7IdHFyV7Ep9LoJe08Cx9eC5HpwfdGREbwamyrJcLwiCBtglCMJxI/A7siy3GlUhCIIK+AiYAeQDsYIgrJTlZp/Eboa1qBhUKtRtBAO1BZssMyJmN5IgMHb3jjY9G4b4TyKx7FtqBXd0NJLnMIUg01bCbbGUHJ5NEFByaDk6IAig4TfyAFl2xNv7BQb0v/6MNlUqNWrVPGysYtPmaYiinuh+LxIS0jI/S3rCr6Snv4rWtRq1aQIDp7TMSLnjR4Wr29XXj0v//UyLZYb/41aG3ngz3zz/BJVJRzgau5+BYzqWlFqlUhGkDaLQXNih8gDJlckAuLUy+zmf2Fy9Gxygn+8AHlh0IzMiZzNv9NXn9JwZP+zAVfKk2r2S/kM6FoFprm3AbGtA69AyM+O5gGvkCG79cgVpv75H3IaNZJXqyP/5GYLc7MrIvUaJCMdnAIx/CCJmKomxuylytwfdg3b/DVlxtK9v3tQ0fzo6dxwJpMuynAkgCMISYD5wThS9rbgYtbd3l6lVtaII48YhZqezJ/4I40/Jr/lnRHn0I2pqzGn7ftkyEg8qaMCRRqdpqJqO4m7PpETdl7iKoaiMVRwyT+Re9RBa4wfs2/cuYuOSEAQLKlURScn3UFJ6N5F95mM0KnQENZU57N9+P6IxEUGjxYV/0GfEndhtVtQthK2HDxlB0s6t6Axtc32IosjChx/j8/tvY9v3X3VY0QOYZBN6zp8C6k48Hv0wj2e8xsNHFWqIbckH+eDA+wx06ssg/yHMHLYAH7f2qS06irSft6M/KlJOIRG3zuxwvcQV6wEIGjToxD5ZkrDZrNgtVmxWC3brye8Tvy0WbFYrdpsVm+V4GSt2q+WUOs3bFuX7eF1bczt2q4WSBiVITz7yC7jaoNcUmPiIotxd/ppAsx50DB167TaPzOOB3sBHsizHCIIwC7hPEIQbgTjgX7IsV/2pagCQd8p2PrRsnhME4Q7gDoDg4K55YlhLStD4nN1i2sX33EHKsiUUffkljPiwU3VHDFvG/rx1zIq4GhedEUmS2Jq1gv7OYUzX92FQbBqyi8A9BTVMCzPj3AJ5lLd3FHNmKw90WtpWMjLvp67uv+yL+QC7PRqpzoLWOQXR2Y5QN5KJ0z9k2UsvsuOzWwAYfsnlTLjmphOzkSOb/mDjF8p1DJzRfuIHFzd3fKIHUXIoFlNjAw7tvByOo9pWja+24yYzH4MP5U3lHS5/tpBlGUmWkJGV30hIsoRdstM7YjAoSYW43HM2e4r3Umyookjez/qC/byT9yU/9f8QFSpkSUKWZGTJjixJSJKELMvN+yVkuXmfJCHZbNjtdiS7DcluR7LZkCQJvwRfEJwJGjoI8+ZSTFIJ2GVkuwySjGyXkG0y2JVzYVP2Vecqj9L6Xz5g04pPsVmsSPY20t11EIIgotZqUWk0qDUaVFotao0WlVqDSqtBrdGidXAhtP8AqM3H68ZPoe80UHdvIvgenDt0SNHLsmwHBguC4AqsEAShP/AJ8BLK6P4l4D/ALX+q2pKPVIuzAVmWPwc+ByUytiNy/RmN+/YB0JRwtL3TnSaeoBJBrUZQa9Bo1FQPH0n/7ZvZExNPxJCBCAgIKIFRQnMtQRBO/m5uytcpkPl9b0cQwCJJCAhMCrsMQYDFR5ORm13G3CwmZaSkViMIQqsmooiIKQQF7Sbx2K8UFy9HEFLQuZtoKHVn6PDXCO4zjZLMdEqzMk7UiVu5nMPrVxPUfxAGoytHt2xAVKm4/MmXCO7fMX91z6AQSg/FUpiVSXj0ybmHLMsnPse3QXHZrBVr6avrS6P1dI8QpZ+EEwFRzVu4O7hT0lhCbm0ufo5+qERVl9gpf07+mf/E/wcB4aQSlyWFqkGWkVG2O4rl5WvPeCqsop0vvn0O//Kzm7GIKhWiSo2X9w2gAXNatXLvqQQEUUBQCaASm78FBK0K8ZR94cZhNLk2ogsynlTKmuMKWotae+q2sk/VXEat0SjK/BTlffx4ZxlTe/D/D52mQBAE4Tmg4VTbvCAIocBqWZb7/6nsGOB5WZYvat5+AkCW5dfaOkdXKBBkWSa5b/d6cLxxw538MXZyt7bZpziXqSkHTtt3XNmLothmhKjFYkYQJAxpiYhWS6c8MP7Mcd9WNVlWRqSNQRFIzq60d49YBSsrQ1d2SI62IAoiKkGFWlSjElSoRJWyLaiVvkE48TI4/ju3LheAG/vdeGKfIDR/N79gREFERAQBRMQTZQQE1KL6tPOpBfXJ84pq1IIaucFMP20vVKpmOUQRQVC+T2yLQvO2SmlbFBFVakS1CpVK3Vynxze8B+cOZ0WBIAiCF2CVZblaEAQ9MB14QxAEP1mWj9PoLQCOtlA9FogQBCEMKACuBs6Zm0XwN19jLShA5fGnVf6WHrDjyksGJDuyzYZstSHbbGC3kVRvYuqY8Ux0dETm5LxAkmVlW+bEfmVbPuU3yujytG0oqamhn9aHyJCZSM3T/pY+rcFqNlGafIyoSy4HFHNB6r7dyLJExKiT9nRZkqivqqSmtBitgx6fsF6td1oLfSPLMiVlZRj7RKPSKrwiJ0bkf/p9/Nuj0QNnX2dUKlXz/IcTI2z5lFnV8ZeGXbaTXp1OhFsEAgJ2yY5NtmGX7NhlOzbJhl22n7Z9vJ1TTTDRHtEM8BrADf1uaP0ae9CD/3G0O6IXBGEgsAjFPVIEfpFl+UVBEL4HBqPosGzgTlmWiwRB8Edxo5zdXH828G5z/a9lWX6lPaHOhtSsBz3oQQ/+F9HWiP6CY6/sQQ960IP/RbSl6Hvij3vQgx704AJHj6LvQQ960IMLHD2Kvgc96EEPLnD0KPoe9KAHPbjA0aPoe9CDHvTgAkePou9BD3rQgwscPYq+Bz3oQQ8ucPwt/egFQSgDzmduN0/g/DFsnT3+v8kLPTKfD/x/kxd6ZO5OhMiy3GJShb+loj/fEAQhrrVAg78j/r/JCz0ynw/8f5MXemQ+X+gx3fSgBz3owQWOHkXfgx70oAcXOHoUvYLP/2oBOon/b/JCj8znA//f5IUemc8Lemz0PehBD3pwgaNnRN+DHvSgBxc4LlhFLwjCYEEQ9gmCcEgQhDhBEEb+6XiwIAj1giA80kr9twRBSBYE4YggCCua0ygiCEKoIAhNze0eEgTh07+RzO6CIGwUBCGt+dvtlGNPCIKQLghCiiAIF51LeQVBGHlK/xwWBGFBK/V/PqVctiAIh5r3n/c+7oTMzwuCUHBK2dmnHOv2Pu4mmc/rvdwN8p7X+7gdmWcIghAvCEJC8/fUVuqf93u5Uzg1D+iF9AE2ALOaf88Gtv3p+HJgKfBIK/VnAurm328AbzT/DgWO/k1lfhN4vPn346fI3A84DOiAMJRU2KpzJS9gOKXv/IDS49tttPUf4Nm/qo87KjPwfEv9f676uJtkPq/3cjfIe17v43ZkHgL4N//uDxR0oK3zci935nPBjuhRMl+5NP82AoXHDwiCcCmQCSS2WlmWN8iybGve3AcEnhsxTz8tZyEzMB8lGxjN35eesn+JLMtmWZazgHRg5JnVu0deWZYbT+k7B9rO0I4gCAJwJbC4G2RqD90icws4V30MZynzX3Avn20fn+/7uC2ZD8qyfPw5TAQcBEHQtdbIeb6XO46/+k1zrj5AXyAXyEPJVxvSvN8R2As40crorIW2VgHXn/KGbgAOAtuBCX8XmYHqP21XNX9/eFz+5u2vgCvOlbzNx0ahPBj1wIJ22pkIxJ2yfd77uKMyN/d/NnAE+BpwO5d93J39fL7u5W7o4/N6H7cn8yllrgA2/V3u5c582k0O/neGIAibAN8WDj0FTAMelmV5uSAIV6LcFNOBF4B3ZFmuF1pKGn7mOZ4CbMCPzbuKgGBZlisEQRgG/CYIQrQsy7V/F5lbOm0L+zo0Yu2ivMiyHANEC4LQF1gkCMI6WZZNrZzmGk4fAf0VfdxRmT8BXkLpv5dQpum3cBZ9fB5kPn6ObruXz9N9ccZpW9h3zvu4uW40itlrZjun6dZ7udvwV7xdzscHqOGk+6gA1Db/3okyIssGqoFK4L5W2rgJZSRtaOM824DhfweZgRTAr/m3H5DS/PsJ4IlTyq0HxpwreVsot7W1PgLUQAkQ+Ff2cWdkPqVMKM3213PVx93Yz+ftXj5bec/3fdyezCimrlRgXDttnNd7uVPXd75PeN4uDJKAyc2/pwHxLZR5ntbNIBcDxwCvP+33onkBCAhHmea5/01kfovTF7HebP4dzemLWJl0z2Jsi/I2n+P4olsIir3Ts41+3v5X93FHZT6ugJp/P4xiMz5nfdxNMp/Xe7kb5D2v93E7Mrs2n/PyDrRxXu/lTl3f+T7hebswGA/EN/9JMcCwFso8zylKE/iS5rctykJPHnCo+fNp8/7LUWyMh4EDwLy/kcwewGYgrfnb/ZRyT6F4KaTQ7F1wruQFbmjuo0PNfXRpS/I2b38L3PWnds97H3dUZuB7IAHFRr+S0xV/t/dxN8l8Xu/lbpD3vN7H7cj8NIqN/dApH++/w73cmU9PZGwPetCDHlzguJDdK3vQgx70oAf0KPoe9KAHPbjg0aPoe9CDHvTgAkePou9BD3rQgwscPYq+Bz3oQQ8ucPQo+h70oAc9uMDRo+h70IMe9OACR4+i70EPetCDCxz/B3BrLLMw2xIPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  251761.0 people (VAP of 206374.0 ) and  142822.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for SE corner\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.5,36.6)\n",
    "pt2 = Point(-76.5,34.5)\n",
    "pt3 = Point(-75,34.5)\n",
    "pt4 = Point(-75,36.6)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  47 tracts w pop 137279.0  to reassign to tracts...\n",
      "[207, 209, 315, 401, 402, 428, 441, 1369, 1389, 2531, 2637, 2659]\n",
      "I have flagged  62 precincts to reassign to precincts...\n",
      "[317, 1047, 1284, 1449, 1454, 2352, 2485, 2488, 2489, 2495, 2501, 2516, 2593]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  137279.0 people (VAP of 109895.0 ) and  78242.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8EdmulFpSy7me7j0LmAWQnJzcqNiuFTHBW8KLJD2nmLjwQDak5fDGkn0Uljn45/lDapifSh1OLn51BVsy8rAI3DOtXy1X1ATbrXx750Q+Wp3Kb3uP8tHq1Mq6qQPqTkajab9UKABtAqqdBs39lVI5IrIImK6UegY4E0BE+gHn1NImw3zPFJEvgNEYpqMjIhKvlDokIvEYs4tmoSJ2fnObgNYePM5F/11BoM1CqcNVWf77V1fw8c1j6RN7wlSRXVDGlow8xvTsyKPnDfbb9Ql/ISrUzq2n9ebW03ozc/ZKVu49xpe3T2BQlwhfi6bxU7QJqH7qNYqISIw58kdEgoEzgO3miB4RsQCPYHgEVW8bKiLhFZ8xFMZms/pr4Brz8zXAVyf1TerAafbFzR0OenemsVRS6nDR37RLR4fZOVpYxhnPLeGFn3dS5nCReqyIa99aBRihkQfG606sIQQHWClzurj27VWUlOt47xrPhGoTUL14MwOIB+aIiBVDYcxVSn0rIneJyO3mOZ8DbwOISBfgTaXUDCAOw2RUca8PlFLzzDZPAnNF5HrgIMY6QrPgaqE1gPVmrttpg+J44+pkAApKHfzv1wMs353NCz/vYt7mw0zqF8OuzAKuGJ3AafXk09XUpGLnsk72pKkLu81CoM2iw0HUgTdeQBuBUzyUvwi86KE8A5hhft4LDK/lukeB0xsob6NwmT1Fc84AlFLkmh3Tn6aeiKQZFmirNF38tOUwj3y5mVlm2GPt0tg4ukUFs2o/zBzbo9ZdzhoN6HhA9eFnfjHNQ8VIsTnjgbz32wG+23SIPrFhDO3medfJmYM789UdE4gOsyMCI7pHNZs8bZWSciefr0sHjExm1VNmajTuhAVpBVAX7UIBVLqBNkP/n1dSTl5JOX8zk5xP7hdT5/nxkcEse3Aqax+ZxuieOrJlQwm0Wbgs2fA4fvrHHUx5ZhEvLdjlY6k0/kqoXYeErgv/3QHUhFSuATSxCai4zMmp/15IbrFp+jm9L/d64c4ZFGDVpotGIiL8+/fDuGNqHzJyinlj6V7+s3A3fxjbo0kypGnaFnoGUDftYgbQXGsAy3dnV3b+0WGBXD2uR5NeX1M7CR1DGNOrEw9MH0CZw8XHbnsDNJoKwvUaQJ20DwXQxGsADqeLT9ekccO7KQCc1j+Gt68dVSOhiab56RcXzqjEKL5an+5rUTR+SKhWAHXSrkxATTUBeODTjZULkQD3n9WfwV10uEFfsCUjlx2H8ylxuFBK6ZwBmiqEBdn0RrA6aB8KoAlNQAePFvHF+nREjFj7PTqGVG760rQ87644QF6JgycuGqo7f00NwgNt5OtF4FppHwqgCUxADqeLN5ft48kftmOzCMsenKrTE/oBk/vH8HFKKnER2vymqUlooI1Sh4typ4sAa7uweDeIdqEATtYN9LmfdvDSL7srj++Z1k93/n7CyB7GXordmQVMHRDnY2k0/oZ7PCB/j3zrC9qFAjhZE9DmDCPR2V9nDGTmOL371F9wOF3c+eE6RGBUot5ToalJZVKYEq0APNEuFMCh3BKARvmJr9iTzS/bM4kOs3PjpF5NLZrmJNh/tJBV+45xat9okhI6+FocjR9SOQMo0+sAnmgXRrFShxExsqGJoYvKHNw3dwMA5w3v2uRyaU6OXtFhzBjamaW7srnwvytYsjOLA0cLWb47m3eW76Pc6ar/Ipo2jU4KUzftYgbgdCksYmSXagj/+/UAGbklvPaHEUwfEt9M0mkai8UivHLlCD5dk8a/vt3K1WaI7QoWbM/0mIxH037QeYHrpl0oAIdLYfMyFnSZw8Uv2zNxuhTLdmfTNzZMd/5+jIhwSXIC04d0JuXAcQ5kF1LicBEcYOX/vt/GlGcWkdwjivOSujAwPoLs/FKW7Mrm3OHxLZqCU+MbdFKYumkXCsDpUl65gG5IzeG299eSnlNcWXbdhJ7NKZqmiQgPCmBK/1jof6Js+pDOfL42nU/WpPJ3M1hfBdsO5fHl7VoBtHW0Cahu2oUCcDgVtnoUgMPp4vxXlgPw2HmDCbZb2ZNZwLUTEltAQk1zEBcRxK2n9ebmSb3YmZnP4dwSAm1WvliXxrzNh/XO4XZAZVYwPQPwSLtQAC6loI6/85yiMl42/fxP7RvNNeMTW0YwTYtgsQgDOkcwoLORevNIXglzU9J4e/l+rpuoZ3htmTCtAOrEm5zAQSKySkQ2iMgWEXnMLB8uIr+KyCYR+UZEaiS2FZEEEVkoItvMtne51T0qIukist58zWjar3YCq0Uq9wJU55OUVJL+OZ/Zy/Zxat9o3r52VHOJofETzk/qwpT+MTz14/bKNJ6atonVIoTYrdoEVAverIyWAlOVUsOBJGC6iIwF3gQeUkoNBb4A7vfQ1gH8WSk1EBgL3C4ig9zqn1dKJZmv70/mi9SFAJ66//3Zhdz/6UYAvrx9Au9eNxqb3i7e5hERHvndICKCArjov8v5v++3caywzNdiaZoJHRG0durt7ZRBgXkYYL4UxnLbErN8PnCxh7aHlFJrzc/5wDagxR3qRUBV0wAul+K6d1YDMPfmcSQldND24HZE75gwfv7zZC4f3Z1ZS/aS/Ph87v9kg9470AbROQFqx6vhrohYRWQ9kAnMV0qtBDYD55mnXAIk1HONRIzk8ivdiu8QkY0i8paIeEyQKyI3iUiKiKRkZWV5I66na6Dc5gBKKSY/s5C92YVMHRCrUzO2UyKCAvi/C4fy7Z0TuXpcIp+sSePvX22u1VyoaZ3orGC145UCUEo5lVJJQDdgtIgMAa7DMOmsAcKBWufQIhIGfAbcrZTKM4tfBXpjmJUOAc/Wcu9ZSqlkpVRyTEzd+XZrvT9VZwDfbzpM6rFihneL5I2rkxt1TU3bYUjXSB49bzC3T+nNh6tS+fvXm30tkqYJCbXrnAC10SCDt1IqB1gETFdKbVdKnamUGgl8COzx1EZEAjA6//eVUp+7XeuIqVhcwBvA6MZ9BS9ws+yk7D/G7R+sBeA/V45osixhmtbPfWf259S+0bz320HKHNoU1FYIC9I5AWrDGy+gGBHpYH4OBs4AtotIrFlmAR4BXvPQVoDZwDal1HPV6ty3116IYVJqNkodLv704Tp+/9qvALxxdTIJHUOa85aaVsbe7EJW7DnKtEFx2G3aGaCtoNcAasebX3k8sFBENgKrMdYAvgWuEJGdwHYgA3gbQES6iEiFR88EYCYw1YO751OmC+lGYApwT9N9rap0MqOAfr0hg7OHdGbNI2cwbZCOHa+pytvL92GzCE9cNNTXomiakNBAbQKqjXo3gimlNmIs3lYvfxF40UN5BjDD/LyMWrZgKaVmNlTYxnLDxF70ig4j9XgRf9ShHTQeOJJXwpaMPBI6hhAdprOLtSUqFoH1zu+atIudwBaLcIYe8Ws8oJTiwc82MjclDYC//W5QPS00rY2wQBvlTkWpw6WTOVWjXSgAjaY2Fu3IYm5KGpclJ3DDqT3pGxfua5E0TYx7RFCtAKqiFYCmXfPR6oPEhgfy+IVDdNLwNop7PKBO2rxXBf2L17RblFKsOZDDqX1jdOffhqmICKpdQWuif/WadsvRwjKyC0rp3znM16JompHwIJ0Upja0AtC0Wyr8QQJt2i7cltEhoWtHKwBNuyUsyIbdZmHVvmMUlenOoa2ik8LUjlYAmnZLoM3KzLE9+G7TIcb83wJeWbgbVT1srKbVU2EC0gqgJloBaNo1j5wzkE9vGcfIHlE8/eMOFu7I9LVImiZG5wWuHa0ANO0aESE5sSOzZibTKzqUf327jZJyp6/F0jQhIXYrInoR2BNaAWg0gN1m4b6z+rMvu5BftutZQFtCRAiz28jXCqAGeiOYRmNyxsA4YsIDefKH7WTkFHNa/xh6x4Tp+DFtgLAgmzYBeUArAI3GxG6z8PylSTz2zRYe/24bj3+3jY6hdgbGh9MlMphgu5WiMid2m4VbJ/fW4cRbEaGBNgq1p1cNtALQaNyY2Dea+fdOJu14EYt3ZrExNZeth/LYk5lNQamDyOAA0nOK+WpdOpckJ3D56AQGdI7wtdiaeggL1ElhPKEVgEbjgW5RIVw1pgdXjalZd/BoEY9+s4UPVh7k/ZUHeOGyUzhnWHzNEzV+Q5hOCuMRvQis0TSQ7p1CeOvaUax8+HSSEjpw54drefzbrezOLPC1aJpaCNNJYTziTUrIIBFZJSIbRGSLiDxmlg8XkV/NrF7fiIjHebCITBeRHSKyW0QecivvKCLzRWSX+R7VdF9Lo2l+okLtzLluNBckdWX28n1Mf2EJy3Zl+1osjQf0IrBnvJkBlAJTlVLDgSRguoiMBd4EHlJKDQW+AO6v3lBErMArwNnAIIw0khUZNx4CFiil+gILzGONplURYrfx3GVJLH9wKg6X4udtR3wtksYDYYHaDdQT9SoAZVAxtw0wXwroDywxy+cDF3toPhrYrZTaq5QqAz4CzjfrzgfmmJ/nABc05gtoNP7Akp1ZAEzuH+NjSTSeqDAB6VAfVfFqDUBErCKyHsjESAq/EtgMnGeecgmQ4KFpVyDV7TjNLAOIU0odAjDfYxssvUbjJ6QeLwJgZI+GWTJdLsVT87Yzd3Vq/SdrGk1YkA2XgmK9y7sKXikApZRTKZUEdANGi8gQ4DrgdhFZA4QDZR6aetpB0yAVLCI3iUiKiKRkZWU1pKlG02KcNbgzAJ+auYW95dn5O/jvoj088NlG5m0+1ByiaXCLCKrXAarQIC8gpVQOsAiYrpTarpQ6Uyk1EvgQ2OOhSRpVZwbdgAzz8xERiQcw3z3uv1dKzVJKJSulkmNi9PRa458M69aBoV0j+ee3W3nky01euRzOXZ3KKwv3cNGIrpzSvQN3f7yejWk5zS9sOyRch4T2iDdeQDEi0sH8HAycAWwXkVizzAI8ArzmoflqoK+I9BQRO3A58LVZ9zVwjfn5GuCrk/geGo3P+e9VIwB477eDDPnHjwx/7CdG/Gs+F7yynL9/tZncovLKc5ftyubhLzYxqV8MT108jFkzk4kOC+T6OSlk5BT76iu0WXRSGM94MwOIBxaKyEaMDn2+UupbDI+encB2jFH92wAi0kVEvgdQSjmAO4AfgW3AXKXUFvO6TwLTRGQXMM081mhaLQkdQ9j3xAzev2EM145PpFdMKGN7dcRus/Durwf400frUEqx80g+t763hj6xYbxy5SnYrBZiwgN569pRlJQ5ue6d1bqjamJ0UhjPSGtaFU9OTlYpKSm+FkOjaTDvLN/Ho99s5enfD+OFn3dR7nTx5e0T6NIhuMp5S3Zm8cd3VjOpbzRvXJ2MTSerbxI2p+fyu5eXMWvmSM4012vaEyKyRimVXL1c/7o0mhbgyjE9CA+0cf+nGzlaWMpb146q0fkDTOoXwz/PH8zCHVk8/t02H0jaNtEzAM9oBaDRtAB2m4Wnfj+MDiEB/OeKEQzpGlnruVeN6cENE3vyzor9zFmxv+WEbMNUrAHocBBV0cHgNJoW4uyh8Zw1uDMWS/35Bf4yYyD7jxbx2Ddb6N4xhCkD9DaZk6EiL7DeDVwVPQPQaFoQbzp/AKtFePHyJAZ0juCOD9ay7VBeM0vWtgm0WbBaRO8DqIZWABqNnxIaaGP2tcmEBdm4/p3VZOaV+FqkVouI6IigHtAKQKPxY+Ijg3ngrAFk5Jbw4SodLuJk0AHhaqLXADQaP+Zwbgn/nredrh2CuWpsd1+L4xXpOcX87cvNbEjN4YyBcTx58VC/yKscFqhDQldHKwCNxk8pKnNww7urKSx18Nlt44kOC/S1SPWy9uBxbno3haIyJ0VlTj5OSWVkjyguHeUpVmTLEhak8wJXR5uANBo/xOVS3PvxBrZm5PHylae0irzDy3dnc8Ws3wix2xjds2Nl+b++20pmvu/XL/QMoCZaAWg0fsiz83cwb8thHp4xkKkD4nwtTr1sycjlhjkpJHYK5bJRCSzacSJyb36Jg1+2eYz12KLoNYCaaAWg0fgZn69N45WFe7hidALXT+zpa3G84onvtxNst/LKVafw8i+76B0TWlkXaLNUmRH4Cu0FVBOtADQaPyJl/zEe+mwTY3t15LHzhvjF4ml9rDlwjGW7szl7SGd+9/IySspdvHLVCM4d3oXgACtPXDSUXjFhvhaTUG0CqoFeBNZo/ITUY0Xc/L81dOkQxGt/GInd5v/js7ySch75cgsdQ+0UlTlxueC1P4xgQOcIXrwsCYWxqc0fMBaBnbhcyusNeW0drQA0Gj8gv6ScG+akUO50MfvaUXQIsftapCrkl5Tz3cZDHC0sI7e4nHKni06hdj5dk0ba8WJmXzuKhz/fxLRBcUwfEg94v+u5pahIClNY5iA8KMDH0vgHWgFoND7G6VLc9dF6dmcVMOePo+ntB+aS6ry/8iBP/rAdgKAACxYRisqc9IkN470bxjCoSwTpOcVcOcZ/9yq4RwTVCsBAKwCNxse8/MsuftmeyeMXDGFi32hfi+ORDak5dO8Ywk/3TCIowEpBqYOF2zPp3zmcPjFhzF62D8AvFntrIyxIRwStjlYAGo2PWbHnKEkJHfjD2B6+FqVWNqXnMjyhA0EBVnKKyrj41RXsySoEDNNKfqmD5B5RjEr0XwVQYQLK1wvBlXiTEzhIRFaJyAYR2SIij5nlSSLym4isF5EUERntoW1/s77ilScid5t1j4pIulvdjCb/dhpNK6C4zElUiP+aJI4XlpF2vJhhZg6DN5buZV92Ic9eMpxnLhnOeUldSO4Rxf1n9fexpHWjk8LUxJsZQCkwVSlVICIBwDIR+QH4J/CYUuoHs/N+CjjNvaFSageQBCAiViAd+MLtlOeVUs+c9LfQaFoxRWUOQuwhvhajVjal5wIw1FQA+7ILSewUysUjuwHwe/Pd39FJYWpS7wxAGRSYhwHmS5mviv3pkRiJ4evidGCPUupAI2XVaNokRWVOgu1WX4tRKxUKYLCpALpEBpN2vLjVjaQrk8JoE1AlXjkai4hVRNYDmcB8pdRK4G7gaRFJBZ4B/lLPZS4HPqxWdoeIbBSRt0QkqpZ732SamFKysrI8naLRtGqKypyE+LMCSMslsVMIkcGGmWraoDjKnC4W7ag/vEO509Xc4nmNNgHVxCsFoJRyKqWSgG7AaBEZAtwK3KOUSgDuAWbX1l5E7MB5wCduxa8CvTFMRIeAZ2u59yylVLJSKjkmJsYbcTWaVoNSiqIyBzaL/2762pyRWyWHcXJiR2LCA7l37gZufDeF9ak5AOzNKuDXPUcBI5jdbe+voe9ff+CHTYd8IXYNQgMNJatNQCdo0K9OKZUDLAKmA9cAn5tVnwA1FoHdOBtYq5Q64natI6ZicQFv1NNeo2mTiAhWi2D10/6/qMxB2vFi+seFV5ZZLcKHN47hqjHd+W3PUS5+dQXzNh/mjOcWc8Ubv5GRU8w7K/bz/abDACzfk+0r8asQaLNit1l0QDg36l0EFpEYoFwplSMiwcAZwL8xbP6TMRTCVGBXHZe5gmrmHxGJV0pVDA0uBDY3WHqNppWyO7OAlxbsol9cGCXlLmLC/SPWf2Z+Cc/9tBOAoAArpQ7DhFM9lk+f2HD+ce5gRvaI4o4P1vGfhbtwKaPutvfXcsQtfeXEPv4zc9choavijRdQPDDH9OKxAHOVUt+KSA7woojYgBLgJgAR6QK8qZSaYR6HANOAm6td9ykRScJYTN7voV6jabPc/L+USj/6ntGhXDTCPzxpft1zlI9Wp9Ip1E6500VJuYtQu5Vh3SI9np96rBiA9OPFTO4XQ++YMD5dk0peiYPI4ADySspZl3qc6UM6t+TXqBUdEbQq9SoApdRG4BQP5cuAkR7KM4AZbsdFQCcP581sqLAaTVtgT1YBe7IKsVmExQ9MITY8kAA/sQE5zWH8Z7eOJzHaCOmslKo1KmmRmWGrf+dwdmcW8PrMkUzuH8M1b61iQOdwukYF8/rivfSPC/cLJRcaaNOLwG74x69Oo2knLN+dzfQXlgBwWv8YukQG+U3nDycUgHsEz7pCUucVlxMeaOOacYmk5xTz696jTOobzee3jee/V43gwekDCLVb+c/C3c0uuzeEawVQBR0KQqNpIYrKHPzl803Ehgfx+W3jiYsI8rVItaKUd+fFRgSRX+pgT1aB2c6YLYzofsKr+7YpfXj6xx3879f97D9axJmD4hjTq4ZRoEUIC7L5RXpKf8F/hh4aTRtnfWoOB48Vcdfpff228482F6OzCrzrJMf3NjryZ37ayYjuHRjfu2Ywuz9OSGRgfAR/+2oLs5ft4+b31rA/u7DphG4AOilMVbQC0GhaiAgzBHFEsP/G/elsKqbDuaVenZ+U0IH7z+rP/Wf15+ObxxEUUHNDW4jdxoc3juHOqX2YNdNYNrx+zmpKyp1NJ7iXhAXaKCht+fv6K9oEpNG0EF07BANUmkv8kUoFkOfdDEBEuH1Kn3rP6xBi589nGsHiXEpxy3trWbnvGJP7tayLaHiQjYLS8ha9pz+jZwAaTQsRFWqnc0QQ+3xk/vCGDiEBRATZ2JqR1+hrlDtdlDqcKKUoKHWQnlNcWbc5PZeXFhgLwgu2HantEs1GqN1GSbkLhx+FqPAlegag0dSFUpB/CCwBYLODLQisdmhEsvbN6blkFZT6dehnEWHKgFgW76w/zo8ndh3J5/xXllNUdsLMUuHuuvbAce77ZEOlCcwXQdlOJIVxEhmix79aAWg0dbH0Gfjl8Zrl1kBDGdgC3V6mcnArV7ZAHGLnaAls3ZvPv4LtzHAlwuJw4xyrW1ubW1tvrm9pngByQ7tG8tX6DDLzSoht4GK1mKki4yICuWxUd9an5rBkZxYPfbaRpbuyGZUYxatmwvtQe8t3P5VJYUrLifRjRdxSaAWg0dRGSR6seBl6TITBF4CzDBwl4Ch1e5V4KC+BskIKc7LIPJ6LxVmKXRycKeVEiBPLujJwNcHo12KrpjDqUxqelEx15RPEmJJiTrMcIGcLxHbtVPM6AcEQHOVxFtQnNozeMaF0jgzi3mn9KCpz8Oe5G5i35TBnDorj5StPIdDmu8inOiJoVbQC0GhqY9UsKMmFsx6HLjU2w9dJYamDM55bjDVImDm2BxHBAZw7vAsWswPC6QBnNUXiKHUrKwFHhWLxpGSqKx/3892uU5pftdy9jbPMo+xDgXfswI91fMEpj8Dk+z1WTewTzXsrD5JbXE5kcACv/mEkxwvLiAwOwGJpuOmsKdF5gauiFYBG44nSAvj1Feh7ZoM7f4AXF+ziUG4Jn94yjmRPeXKtNuNlD20CYRuJy1VVsZhKY86S7Xy5Zh+fXH8KNldZTeWz7Dk4uMLjJb9an84Hqw7SMzqUAOuJzj4q1N5S36pOwsyQ0DopjIFWABqNJ1LeguJjMMnzKLcuth/OY/ayfVyWnOC58/cXLBawBEFAVTv/QXs5u2x2bL1O9dzuwArYt6RG8cIdmdw7dwOjEqN4fWYyIT6w8ddHWKBh99cmIAO9DK7RVKe82LD995wMCQ1LU3Ekr4R7Pt5ARJCNh84e0EwCNi/lThd2Wx1dQ0x/yM8wzGMmJeVObno3BadLcWlyQmX2MH9Dm4CqohWARlOdNXOgMBMmP9CgZgePFnH6s4vZdiiPh2cM9BuzR0Mpc7iqmG9qEDvQeM/aUVlktUjliP/euRv4bqN/ZAGrTphd5wV2RysAjcYdRyksfxG6j4fEiQ1qev+nGxDggxvHcElyQvPI1wKUOV11RyiNMWc2mdsqiwKsFj65ZRyf3zaeXtGhvPfbgWaWsnGcSAupw0GAXgPQaKqy7j3DvHHBKw1qlpFTzMp9x/jztH4eA6K1Jsqdqm4F0KEH2IKrzAAA+plpIzuG2inz0522NquF4AAr+SU6HAToGYBGcwJnOSx7AbomQ68pXjdTSvHgZxsJCrAwY1h888nXQgQHWDheVIbLVUtMaIsFYvpBljEDWL47m5cX7CLP7FQPHiuid4wPvZvqITTQRmGZNgGBFwpARIJEZJWIbBCRLSLymFmeJCK/ich6EUkREY+rZSKyX0Q2VZznVt5RROaLyC7zPcpTe42mxdj4MeQeNGz/DQj1sONIPkt3ZfPnaf3pXS13bmtkTM9O5BSVs+NIfu0nxQyEzO0cLSjlqjdX8uz8nazedwyAvJJywoP8cxEYDFdQHRHUwJsZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4NqYopZKUUsluZQ8BC5RSfYEF5rFG4xucDlj6LMQPN3z/G8DiHVkAnDu8S3NI1uKM6WW4rq7ce7T2k0xPoOL845VFQQFWth/OIyjAStrxouYWs9GEBem8wBV4kxNYARXxawPMlzJfEWZ5JJDRwHufD5xmfp4DLAIebOA1NJqmYcvncGwvXPZegwO9LdmVRf+4cDpH+meSl4bSLSqEblHBrNhzlGsn9PR8kukJ1LFoX2XRVW+uBCDAKgzr1qG5xWw0oXadFKYCrxaBRcQKrAH6AK8opVaKyN3AjyLyDMZMYnwtzRXwk4go4HWl1CyzPE4pdQhAKXVIRGJrufdNwE0A3bt39+5baTQNweWCJc9A7CDof06DmhaVOVi97zjXjO/RTML5hmmD4vjfrwdIPVZEQseQmieYnkAhOTvpE5vI7swCrhidQNcOwfxhbA86hPivC2x4kI2MHJ0WErxcBFZKOU1TTzdgtIgMAW4F7lFKJQD3ALNraT5BKTUCOBu4XUQmNURApdQspVSyUio5JqZlk0do2gnbvoLsHTDpPmOBswFsTs+jzOmqkgO3LXDzpN5YRHhpwS7PJ7h5AvWOCaVXdChPXDSMO6b29evOH/QisDsN+rUrpXIwTDXTgWuAz82qTwCPi8BKqQzzPRP4wu28IyISD2C+Ny4AuUZzMlSM/jv1hUEXNLh5/86G6+OKPXXYy1shnSODuHZCIp+sSWPWkj01T3DzBOrRKZTU40U+SfHYGMJ0XuBK6jUBiUgMUK6UyhGRYOAM4N8YNv/JGAphKlBjqCAioYBFKZVvfj4T+KdZ/TWGEnnSfP/qpL+NRtNQdv4ARzbDBa81Kr5+RJANESN8QlvjvjP7k3qsiCd+2M6AzhFMqp6+MWYg7FvC5HExzFqyl7kpqVw9LtEnsjaEsEAbOcXl3Dt3PSF2K6F2GyF2G6GBVoIrj62EBtqqHIfYrdhtFgKsFqwWwWYRpBGJgfwJb9YA4oE55jqABZirlPpWRHKAF0XEBpRg2ulFpAvwplJqBhAHfGE+JBvwgVJqnnndJ4G5InI9cBC4pOm+lkbjBUrB4qcgKhGGNu7nJyKMSuzI8j3ZTSubH2C3WXj20uHseaWAuz5ax28Pn141ln9Mf9j4EeO72hjbqyNPz9vB6QPjKnMf+yun9o1h0Y4sVu49RnG5k8JSB6WOxilwm0WwWYUAiwWbVbBZLZVlVhEsImD8Q0TMdxCk0tegSrlbXUU5GDuYn780qcEJeuqVv74TlFIbgRrxcJVSy4CRHsozgBnm573A8FquexQ4vYHyajRNx+6f4dB6OPclIzRzI+nRMYRNabkopRo8IlRK8fnadL7akEFSQgfuOaOvX40qQ+w27juzPzf9bw3Pzd/JQ9MHnJDP9ASS7J08/fvhTH9hCX/5fBPvXtewAHotzcS+0fx4T9WlSIfTRVG5k+IyQyEUVbyXOykqdVJY5qCo1EG5U1HucuFwKhwuhcPpwuFSlDsryk7UOV0KBbiUAgUKhVLGuKPyM8YxVY4r2hmyHS0oZfnuHPYfLWp5BaDRtEkqRv+RCTD8ipO61MgeUXyyJo21B48zsof34Z+VUjz+3TZmL9tHdJidJTuzGNOzIxP6+FcoiWmD4rg0uRuvL95Lv9hwLh7ZzahwiwmUMHI0t0/tw1PzdrAvu5Ce0f67E9gTNquFCKuFCD/cwPbFujTu+XgD0WFNv7iuQ0Fo2if7FkPaKphwl5EO8SQICjDMIg0dub+4YBezl+3j2vGJLH1gKh1D7byzYv9JydIciAhPXjSMHp1CeOrH7Sc2eVV6Am0H4KJTumGzCI9/u7X2MBKaBnMkrxSA6PDAJr+2VgCa9snipyGsM5wy86QvtWr/MYIDrCQ1YPPTNxsyeOHnXfx+ZDf+ce4ggu1WLjqlKwu3Z1Jc5n/eNBaL8NofRlJU6uQvn2+qKDQ9gQwF0DkyiL/MGMiC7Zks3d321kR8xYGjhXQMtTfL7EQrAE3748AKOLDMGP0HnLxNtV9sGMXlTvZmF9Z53ub0XK57ZzVXv7WKv3y+iYHxETx50dDKmcPk/jE4XIqfth4+aZmag4HxEdxwai+W7spmeUUHb8YEquCK0UYY7M3puZ4uoWkEe7Oaz6SmFYCm/bHkaQiNgZHXNsnlpg8xIoD+vO1Iree4XIpnftrBL9szyS0qY3TPjrx0eRI2t7DLE/tEExcRyPyttV/H19w0qRe9Y0K55+P1fL42DVWRHaw4p8p5Fj9ayG7NOF2KrRl5laG2mxqtADTti7Q1sOcXGHcH2D2EOGgEnSODiA6zs+Ow5+iZuUXlPPjZRhbtyOKvMwby1R0TeevaUfSt9kctIkzoHc2KPUf91oYebLfynytHkJlfyr1zN3AkyIwVlL0TMDKD2a0WdhzO86GUbYedR/LJL3WQ3KN5dpprBaBpXyx5CoKjYNT1TXrZqQNi+XpDBnNTUqt03vuyC7nwv8v5fF06N03qxQ2n1hJczWRMr44cKyzj4DH/jaY5MD6CPrFhdO0QTFSPYUahmR0s0GbljEHGs9hXj0lMUz8pB4xoq8mJWgFoNCfHoQ2wcx6MvR0Cm3ZK/fdzBzOyRxQPfLqRG99NIT2nmNnL9nHuy8s4XlTGhzeO5eEZA+v1FLKasYjy/DxjVanDyaAuEQRG96ziCQTGDuJQu41LXvuVTWl6LeBkWLP/GDHhgXT3FJCvCdAKQNN+WPI0BEbA6Bub/NJhgTY+vHEs/zh3EAt3ZDLhyV/417dbGdI1gm/unMjont7tD5jQpxNWi/j1OkCpw0lpucvYwFTNEwigV0wYn902nuyCUm54dzVljdxlqzFmAMk9opptc6DeCKZpHxzZCtu+gUn3Q3CHZrmF1SL8cUJPhnSNZPX+Y5zaJ4ah3SIbdI34yGA6RwSx7mBOs8jYFHy48iCZ+aVcOz7RKDBjArkzx9zPULGz1n6Sey3aI0fySkg7XnziOTcDegagaR8sfQbsYTD2tma/1ajEjtx2Wp8Gd/4VhNitLNudzZ8+XEdOUVkTS3dyFJU5ePmX3Yzr1YkJfToZhR48gZbvzmZgfATLHppKVKju/BtDyv4K+7/3u8sbilYAmrZP9i7Y/Lmx8BvSfH9MTcWLl5/CZckJfL0hg4n/XuhX5qB5mw9ztLCMP53et0ZMoApPIIBh3TqQXVBKcEDDI6xq4M2le7n9g7UE2iwM7hJRf4NGohWApu2z9FmwBcG4O30tiVcM6hLBv38/jB/uOpVeMaHc/dE6jhf6x0xg9jIjBeRz83ewqyJpvFtMoArOHtKZrPxSZs5eyeFcnX2rIaTnFPPED8aayl1n9CXA2nzdtFYAmrbNsX2wcS4k/xHCWldGuYHxEfztd4MoLHOycp9/JJy5fFQCM4Z2ZldmATNeWsr3mw7ViAkEcPbQeJ67dDjrU3M48/nFfLuxoSnD2y+vLNyN1SIsf2gqt53Wp1nvpRWApm2z7Dmw2GD8n3wtSaOwmFaWYLt/+GvMHJfIf68ayYJ7JxMdFsiDn25EidTwBAK4aEQ3frhrEr1jw7jjg3V8uibNR1K3HrILSvlmfQZnDe7cInkVtALQtF1yUmH9hzBiJkTE+1qaRlGRgOXPczfw4xb/iRG0/2ghWfmljOnV0VgLqBYTqIKe0aF8fNM4hnaN5LXFe1DKP3c4+wtvLNlLYZmDu07v2yL3q1cBiEiQiKwSkQ0iskVEHjPLk0TkNxFZLyIpIlIjC4SIJIjIQhHZZra9y63uURFJN9uvF5EZTfvVNO2e5S8a7xPu9qkYJ8Og+AgemN6f6DA7N/9vDY98uemE7d2HPPnDdmLDA3n20iSjoJaYQGBkFrtyTHd2ZxawPrVmveYER/JKiI8Mpk9sWIvcz5sZQCkwVSk1HEgCpovIWOAp4DGlVBLwd/O4Og7gz0qpgcBY4HYRGeRW/7xSKsl8fX8S30OjqUr+YVj7LiRdAR0SfC1No7FYhNtO68NXd0zg2vGJfLDyIGe+sMSnNvX0nGJW7z/ONeMTiQw2QxR78ARy55xh8QQFWPhoVWoLSdn6KHe6+G3vsRZNplOvAlAGBeZhgPlS5qvCPykSI0l89baHlFJrzc/5wDagaxPIrdHUzfKXwOWAiff6WpImIdBm5dHzBrPy4TMY1aMj93y8/kRI5hYm3wxT0cXdRu3BE8idiKAALhmZwGdr03SMoFr4buMhDueVcN3ExBa7p1drACJiFZH1QCYwXym1ErgbeFpEUoFngL/Uc41EjNzCK92K7xCRjSLylog0T7QjTfujIAtS3oJhl0LHuoOvtTZiwgN54+pkekWHce3bqxj4t3lc985qjuS1nKtlQlQIFoFdmQUnCj14AlXnT6f3xW6z8P++29bu1wKW785m0lMLuXfuesBID/rG0r30jgnltH6xLSaHVwpAKeU0TT3dgNEiMgS4FbhHKZUA3APMrq29iIQBnwF3K6Uq4sS+CvTGMCsdAp6tpe1N5hpDSlZWlldfStPO+fU/4ChpM6P/6kSGBPD+jWM4tW8MxeVOftmeyV0frcPpIYR0SbmTY4VlTdrhhgbaGNA5gjUHjp0o9BATqDox4YH86fS+/LztCM/P30lBqYMF24741Ua35kYpxbu/7ueqN1dy8FgRGTnFAHy78RBbMvK4eXJvLJaWy6XQIN8ypVSOiCwCpgPXABWLup8Ab3pqIyIBGJ3/+0qpz92udcTtnDeAb2u55yxgFkBycnL7HjZo6qfoGKx+EwZfaHRIbZTosEDeunYUAHNTUnng043c/v5aHjp7AN07hvDpmjT+9tVmnC6Fw6UItVsZEB/BXaf3ZVi3SCKCAk6qo+neMYR5Ww5T5nBht5njSA8xgapzw8Se7Dicz0u/7OalX3YDEB5kY9OjZzValtbE3JRU/v7Vlspjp0tRXObkie+3MSg+gotHdGtReepVACISA5SbnX8wcAbwbwyb/2RgETAV2OWhrWDMDLYppZ6rVhevlDpkHl4IbD6J76HRGPz2KpQVwKT7fC1Ji3HJyG7kFZfzxA/bmbflMLHhgeSVlFPqcHHL5N5Eh9lJO17M/K1HuPqtVQB0iQzimvGJXD66+4mF3AZw4YiuzNtymK83ZPD7kWanFdMfNn5keALVEnDPZrXw3KXDuWxUAiv2HCVl/zHWHjzeyG/e+vhleyYAn906jj99uJ5OoYG8vmQPGbklPH9ZEtYWHP2DdzOAeGCOiFgxTEZzlVLfikgO8KKI2IAS4CYAEekCvKmUmgFMAGYCm8w1BICHTY+fp0QkCWMxeT9wc1N9KU07pSQXVr4OA34HcYN9LU2LISLccGovpg2K4/tNh/n3PMMM88/zB3P1uMTK8/4yYwDfbDjE8cIyftmeyRM/bOeVhbt569pRDQ44Nm1gHEO7RvLCzzs5d3i8sV/B3RMooYZXeBV5x/bqxNhenXjyh+2VSU/aOmnHi1i4PYuB8RGM6B5FTHggm9JzWbQzk3OGxTOmV6cWl6leBaCU2oixeFu9fBkw0kN5BjDD7RyPKk0pNbOhwmo0dbJyFpTmGiGf2yE9OoVy62m9mZuSyr7sQs4d1qVKfaDNWjlav3FSLzan5/KnD9dx9VurePOaZMb3jvb6XhaL8MD0/sycvYoPVh7kjxN6VvUEqkMBuBMeZKPM4aKozEGIn+x2bi5eWrCLMqeLWyb3QkSIDQ9kfWoOgTYLD88Y6BOZ9E5gTdugNB9+ewX6ngVdknwtjU/58rYJfHPHxHrDMA/pGslHN4+lW1Qw172zmi0ZDcveNbFPNON7d+I/v+ymoNThlSdQdSoyXbWHzGG5xYb77Jiexkg/NNBQeLdM7t0iYR88oRWApm2wejYUH4fJD/haEp8TGRLgdS6C2PAg3rthDKF2Gy8tqLGMVyciwgPTB3C0sIzZS/d55QlUnakDYrEILN3lmz0NLcn+7CIigmyVIbL/9rtBPHfpcG6b0ttnMmkFoGn9lBUZrp+9pkC3ZF9L0+qIDQ9iUr8Y1h3MabC7aFJCB84e0pnXFu8hPae41phAtREaaMMigqJtO/gdyi1mx5F8rh2fSGSIsejeMdTORSO6VcZ78gVaAWhaP2vnQGGWHv2fBCN6RJGZX8qerIL6T67GX88x7NePfr0FYgfUGhOoNlxKIZ6XCtsMecUOAF76ZTd//8p/HB61AtC0bspLjKBvPSZCj/G+lqbVctbgOKwW4bO16Q1u2y0qhFsm92b+1iNkBZk7r2uJCeQJxYmw122RNQeOc+5/llUepx4r8qE0VdEKQNO6Wf8e5B+Cye3T86epiA0PYlLfaL5Ym86KPdn8uudog8xBF480Qnz9mGVGdKklJpAn2npUiC/WpeFwuvjXBUO478x+PPK7QfU3aiHatt+Vpm3jKINlL0C3UdBzsq+lafVcmpzAre+v5co3jHBdN0/qxV+8dE8sdbgAyLbFNdgTKCjAQonZvjqHcourBI/rHxdOp7BAr6/ta5RSrD2QQ6+YMGaO7eFrcWqgFYCm9bLxI8hNhXOeA2nDNoQWYvqQztw5tQ/5JQ7ySxy8sXQvZw+NJymhQ71tn5u/k7BAG38Y1xP2NswTKDwooNacx1fPXlUl6FyAVXjp8lN46Zfd2G0Wnr1keIvFzm8MaceL2Xooj/vO9M+wJFoBaFonToeR7D0+CfpO87U0bQIR4c9n9geMkM8Ld2TyxpK9jOnVkZcW7CY6zM7ffjeICX1qbhjbm1XIiB5RRIcFehUTyJ2B8RFszsjzWNfB9Jj56Kax3PvxejJyS7j1/bWEBdooKHWw9uBxv1YAcRFBhNitpOe0XLTWhqDXADStk82fwvH9xq5fPfpvcsKDAjhnaDzfbTrE37/aQs/oEDLzS/nj26uZ8eJSvlxXdbF4UHwEm9JyjIMGegKN6dmR7Yfz2JxeczPYODM8wsgeUSx5YAqT+8UQFGDh/rMqFJWj0d+xOXlpwS56/uU7Jj+9kKIyJ1EhDY+31BJoBaBpfbicsOQZiB0M/XUm0ebitinGDtWpA2L54Max/O/60UwbFMfWQ3k8/eOOKuGnI4JtOCqOK0JCZO3w6j5XjelOdFgg932ygRvmrDbcSU0qI41iBJKbc91otv1zOleN6Q5AgR8qgKIyB8/N34lScCjXGPlP6hfjY6k8oxWApvWx9Us4usuI+GnRP+HmIj4ymGUPTmH2NckEWC0M7hLJK1eN4F8XDCE9p5h/fH3Cn91utVDqcBmeQ5UKwLt1gA4hdv7vwqFsP5zPz9syeWfFft5cuheAcmdNFyERwWa1EGK3VmYn8xcO55Zw+rOLa5RvO+TZxOVr9F+PpnXhchmj/+h+MOh8X0vT5hERpJqJbebYHlw5pjsfrkqtXLyNCrVT5nA1OibQtEFxXHiK4Uo6oHM4j3+3jVv+t4a3lu1jYHwENg8bBcKDbH5nArr1/TWVo353vljX8P0VLYFWAJrWxY7vIHMrnHofWHy3hb69c+Xo7jhdih+3HAagX5yxELs1I+9ETKAG7AUAeOKioXx26zi+uXMit57Wm5+3HUEEXrgsqYYSAioXgv2JUR7Cak/uF8O907QXkEZzcigFS56GqJ4w5GJfS9OuGdwlgh6dQnh/5UFG9oiib2w4AOtTc4y49g30BAIICrAysofRgT44fQB3Tu2DRYSggJqKvszhYk9WYdXE9H7AA2f158OVB8k3FVNcRCBzrvMuNLYv0DMATeth13w4tAFO/TNY9djFl4gI953Zn80ZuUx7fgkXvLIcixgKAGhUTCB31hw4zksLdvPApxtZte9YjfqNpseRw8MagS+xWS18dPPYyuP/XDnCh9LUj/4r0rQOlIIlT0Fkdxh+ua+l0QDnDu9CYqdQlu/J5skfDHt/Rcz7Kp5A3cc06LqvL97DEz9sJ8AqlDsVX2/I4IvbxnNK96jKc+IiggC44JQutV2mxfl56xGe/3knW8w9DT06hXg0CfkT9c4ARCRIRFaJyAYR2SIij5nlSSLym4isF5EUEfE4zxGR6SKyQ0R2i8hDbuUdRWS+iOwy36M8tddoANi7CNJWw8S7weqfPtXtkaHdIrnx1F70jgkFMDKDQYM9gSooKXfy/M87OX1ALOv/fibz7j6V7h1DuOjVFVz3zmqKygzTSnxkEFaLkHa8uMm+y8ny5LztlZ3/4xcM4b3rG6b4fIE3M4BSYKpSqkBEAoBlIvID8E/gMaXUDyIyA3gKOM29oZlH+BVgGpAGrBaRr5VSW4GHgAVKqSdNxfAQ8GBTfTFNG2PJ0xAeD0lX+VoSTTWsFuG7P51KVn4pCWaGr8Z4AgFsSs+lpNzFFaO7ExpoY0DnCD69dRzP/LiDuSlpLN99lGmD4rBZLcRHBrH9cH4zfKPaKSl3kp5TTOqxItKOF5uvIlKPF7M7s4BQu5WPbx7HkK7eJeTxNd7kBFZARTCOAPOlzFeEWR4JZHhoPhrYrZTaCyAiHwHnA1vN99PM8+YAi9AKQOOJ/cvhwHKY/iQEBPlaGo0HggKsJzp/aLQnkMtV06bfIdhemS/YPWbQ2UM6M3vZPg4eLaJ7p5Aa7RpDmcNFRk4xqceNDv5ER2908ln5pVXOD7AKXTsE0y0qhCtGJ3Dm4M6tpvMHL9cAzJH8GqAP8IpSaqWI3A38KCLPYJiSPAVj7wqkuh2nARXzojil1CEApdQhEYmt5d43ATcBdO/e3RtxNW2NJU9BaAyMuMbXkmgaQiM8gY4XGR38/qMnIoAeKyzjnRX7ARjV84RNfXK/WN5Yuo9V+4+dtAKYm5LKSwt2kZ5TXCU8tc0idOkQTLeoYKb0jyEhKoRuHY0Ov1tUMHHhQVhacTIDrxSAUsoJJIlIB+ALERmC0Snfo5T6TEQuBWYDZ1Rr6unJNGjZXik1C5gFkJyc7F9L/prmJ3W1Yf+f9k+wN80oT9NCxA4wIrYW50BwB6+adAw1Qj0P6BxRWZZdYIy6hyd0oGd0aGX5+ysPEBZoY+oAj2NHr1m4I5MHPt1Ij04h3Dm1LwlRRgef0DGYzhFB2Kxt11myQV5ASqkcEVkETAeuAe4yqz4B3vTQJA1IcDvuxglT0RERiTdH//FAZkNk0bQTljwFwR0h+XpfS6JpKI3wBAqxGz7/FYu9AP/3/TaiQgK4dXKvyrL8knLmbz3CHyck0jHU3mgRlVL88e3VADxw1gDOGRbf6Gu1RrzxAooxR/6ISDDGKH87RkdekYVjKrDLQ/PVQF8R6SkiduBy4Guz7msMJYL5/lUjv4OmrZKxDnb9BONug0D/DfmrqYVGeAKFBhpj0qIyZ2WZUnC8qJwOIXZSjxWRX1LOnBX7cbgU04ecXIdd4bXTLSqY0T3922WzOfBmBhAPzDHXASzAXKXUtyKSA7woIjagBNNOLyJdgDeVUjOUUg4RuQP4EbACbymlKkL9PQnMFZHrgYPAJU35xTRtgCXPQFAkjL7J15JoGkMjPIFCzRnA3R+v55ftmdgswq97jwJw+azfqpwbHxnEiO4dvL52Zl4J32w8xMq9R8kvcXBqv+jKjWTv3zCGmPDWk2msqfDGC2gjcIqH8mXASA/lGcAMt+Pvge89nHcUOL2B8mraC0e2wPZvYfKDhhLQtD4a4QkUFWpndM+OZOeXsjEtB4dLERxgpbjcWeW8/3fhEJISOniMEeSOUorP16bz+bo0ft1zFJeCntGhBAVYeWreiXDVgbb2GVdK7wTW+CdLngF7GIy5xdeSaE6GBnoCBVgtzL15XJUyp0ux/2ghm9NzefKH7cRHBnHVGO/y6z45bzuvL95Lj04h3DGlD+cldaGPGbfo4NEi5vy6n/AgG3ER7W/0D1oBaPyRrJ2w5Qtj129I+7PLtika4QlUHatF6B0TRu+YMB7/bhu9Y7xbD3pz6V5eX7yXP4ztzr/OH1JjttC9Uwh/+92gRsnUVtAKQON/LH0WbEEw7g5fS6I5WU4iJlB18kvKycovpVdMGOVOF1n5pRSUOigtd1HqcFLqMN/LXezNLuTpH3dw9pDOPHZezc5fY6AVgMa/OLYXNn1imH5CayYf17Qy3D2BTlIBHDhaBMCLC3by73n1LyyP69WJ5y9LwtqKN2o1N1oBaPyLpc+BxQYT/uRrSTRNQSNjAnmie6cQLhnZjQCbhdjwQGLDg4gMDiDQZiEwwEKgzYrdZjGObRZ6dArVnX89aAWg8R9yDsKGD2HkHyG8s6+l0TQFjYwJ5ImIoACevmR4EwilqaDt7nHWtD6WvQCIsfiraTvEDDTWADR+h1YAGv8gLwPW/Q+SroTIbr6WRtOUnGR2ME3zoRWAxj9Y/hK4nDDxHl9Lomlq3D2BNH6FVgAa31OQCWvehmGXQceevpZG09Q0MjuYpvnRCkDje1a8DM4yI9m7pu3RhJ5AmqZFKwCNbyk6Bqtnw+CLILqPr6XRNAdN6AmkaVq0AtD4lt/+C+WFMOk+X0uiaU60J5BfohWAxncU58DK12HgeRA70NfSaJoT7Qnkl+iNYBrfsf59KM2D1FXwysmFCdD4ORX2/zVva08vP0IrAI3v6DYKBl1AA9NEa1oj9lBIXwPBUb6WROOGVgAa35Ew2nhpNBqf4E1O4CARWSUiG0Rki4g8ZpZ/LCLrzdd+EVnvoW1/t3PWi0ieiNxt1j0qIuludTOqt9doNBpN8+HNDKAUmKqUKhCRAGCZiPyglLqs4gQReRbIrd5QKbUDSDLPsQLpwBdupzyvlHrmJOTXaDQaTSPxJiewAgrMwwDzVWm0FSPTwqXA1HoudTqwRyl1oHGiajQajaYp8coNVESspoknE5ivlFrpVn0qcEQptauey1wOfFit7A4R2Sgib4mIx9UhEblJRFJEJCUrK8sbcTUajUbjBV4pAKWUUymVBHQDRovIELfqK6jZsVdBROzAecAnbsWvAr0xTESHgGdrufcspVSyUio5JibGG3E1Go1G4wUN2gimlMoBFgHTAUTEBlwEfFxP07OBtUqpI27XOmIqFhfwBqDdQTQajaYF8cYLKEZEOpifg4EzgIqoTmcA25VSafVcpsYsQUTi3Q4vBDZ7KbNGo9FomgBvvIDigTmmF48FmKuU+tasq2HXF5EuwJtKqRnmcQgwDbi52nWfEpEkjAXl/R7qNRqNRtOMiOHk0zoQkSygtXsRRQPZvhbCj9DPoyb6mVRFP4+aNPSZ9FBK1VhEbVUKoC0gIilKqWRfy+Ev6OdRE/1MqqKfR02a6pnoaKAajUbTTtEKQKPRaNopWgG0PLN8LYCfoZ9HTfQzqYp+HjVpkmei1wA0Go2mnaJnABqNRtNO0QpAo9Fo2ik6IUwLICIfA/3Nww5AjlIqyYyR9DqQDLiAu5RSi3wiZAtTxzMJAN4ERmD8Pt9VSj3hGylbjjqex1XA/W6nDgNGKKXWt6yELU9tz8SsG4bxtxOB8bczSilV4gMxW4w6fiOJwDZgh1n3m1LqFm+uqRVAC1BH7oQbzfqhIhIL/CAio8z4SG2aOp7JJUCg+UxCgK0i8qFSar8PxGwxanseSqn3gffN8qHAV+2h84fan4kZg+w9YKZSaoOIdALKfSNly1FPDpY9FcqxIWgF0IJ4yJ0wCFgAoJTKFJEcjNnAKp8I6AM8PBMFhJp/5MFAGZDnI/FanHrya9Qbebct4uGZnAlsVEptAFBKHfWVbL6gATlY6kWvAbQs1XMnbADOFxGbiPQERgIJPpPON1R/Jp8ChRghwg8CzyiljvlKOB9QV36Ny2iHCoCaz6QfoETkRxFZKyIP+FA2X+DpN9JTRNaJyGIROdXbC+kZQBMhIj8DnT1U/VUp9ZX5ufoI7i1gIJCCEeNoBeBoTjlbkkY+k9GAE+gCRAFLReRnpdTeZhW2BWjk86hoOwYoUkq1qai5jXwmNmAiMAooAhaIyBql1IJmFbYFaOTzOAR0V0odFZGRwJciMlgpVe/MWSuAJkIpdUZd9W65E0a6tXEA97idswKoL7Naq6ExzwS4EpinlCoHMkVkOYZZrNUrgEY+jwo8ZdRr9TTymaQBi5VS2eY532M4DbR6BdDIfqQUI3c7Sqk1IrIHY5aUUt/9tAmo5aiRO0FEQkQk1Pw8DXAopbb6SkAf4CmfxEFgqhiEAmM5kX+ireMxv4aIWDAWxz/yiVS+xdMz+REYZv792IDJQHv5u/HUj8SY4foRkV5AX7wcMOkZQMvhaQQXC/woIi4gHZjZ4lL5Fk/P5BXgbYwEQQK8rZTa2NKC+YjaRvmTgLS2YAZrBDWeiVLquIg8B6zGcBr4Xin1nS+E8wGefiOTgH+KiAPDfHqLt+tmOhSERqPRtFO0CUij0WjaKVoBaDQaTTtFKwCNRqNpp2gFoNFoNO0UrQA0Go2mnaIVgEaj0bRTtALQaDSadsr/B/Zptor7sCVUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n"
     ]
    },
    {
     "data": {
      "image/png": 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IQmAOvqPzN/yFfamIeIFkoOHh+C6gX4PnfYGC40ocgD7do4GWz4Jf+dQynHbB5TGM7pPAH88fzcjeWuSVUq1X168+GAt9IL1uUvxH8ohINHAKsB54C5jlnz8EiAAa325lGTBYRDJFJAK4GHinvcI3Z3Oh75TC8h2HKK12HbX8u+P78sj3xzEpIwmXx3DVCZm8e/N0LfJKqTaraxJu2EwcLAI5ok8DnvH3oLEBrxhj5vsL95MishqoBX5ojDEi0hv4tzHmDGOMW0RuAj4E7MCTxpg1HfRZ6o3sncB54/rw5ordjLn3I+bfPP2I5pgIh42zxvTmzNFp7Cyqoq//G4BSSrXVql0lJMY465uGg0lYD4GQced79dPb/njmcb+eUko1xeXxcsIfP2NM30T+/cMmRyHocMcaAiH4vmO0ozNG9wIgJsK68SeUUuFvX2k1hWU1TBmQZHWUJoV1oX/s0glcMqk/lbUeXlq6g2D89qKUCn29E6JJiHaycV95yytbIKwLPcD3/FfB3vnGKt5audviNEqpcGSzCZMzk/hk3T7KmugAYrWwL/QT0rvz9V2zAFi9u9TiNEqpcHX19EwOVtSyYMPxXfDZEcK+0AM4bL6PmZYQZXESpVS4Kizzda9MjA6+O8p1iUJfNxxxMPZvVUqFh/9+vY30HjFMH3T01fhWC/vK905eAVP+8CkAQ1ObvtGIUkodj7UFpSzbdojLp6RjC8KxbsK+0P/q7dUADOsVx6TM4Oz6pJQKbc9+s40op40LJvRreWULhHWhN8ZwqNLFxIzufHDLiYgE355WKRXaSipdvLWigHOz+pAQE3zt8xDmhb7GP5ocoEVeKdUhFm0+QJXLwwXZwXk0D2Fe6KOcdiZmdGdjYXBexKCUCn21/gPK7kF6NA+tHKY4lJRVu3h0wWaWbTsEQFWth2gdCkEp1c6M/xYbwdxqEHaF/j9fbWVnUSUvLN1BrdvLiLR4fjgtXYu8UqpD1I2sErxlPswK/Z6SKn4zfy0A/ZNi+OmpQzgnq3dQ72mVUqqjhVWhv/XllQC8/qNpfP7tfn49fy0/eXklS+6eTWq8XhWrlArcf7/exi/fXkO0047dJtjEd2/Y7jER/oeTxJgIFm4oBCCYjyfDqtBHOX3NM5f++xuqXYd73CzcUMhFE/tbFUspFYIS/EMZ1Hq8XDE5A4/XUFxZy6FKF8WVtWw5UE5xhYuyGjfRTjuJ0REWJ25eWBX6By4Yy8OfbSJ/VzEXT+zP7OE9mfDbT44o+kopFYizx/bm9eW7WbHjEDfPGkRiTNOFvNbtxWtM/YFmMAqr7pU9ukVy79kjeeOGE7hwYj8Sop1EOmx8ur6wyXvHKqVUc0SE208bQlm1m3fyCppdL8JhC+oiD2FW6Btz2G0MT4vni2/3s3RLkdVxlFIhpu7udHFRod34EdaFHuDM0WmA78YjSinVGgnREThsQv6uEqujHJewL/SnjkgF4EB5jcVJlFKhJiUukvPG9eG5b7aTv6vY6jhtFvaF/t531wDw14vGWpxEKRWK7pw7jJ5xUZz9yCL+/OF6XJ7Q69wR1oW+qtbDQv9tvW59OY9THvzc4kRKqVDTo1skz10zGYBHF2wm+7efUFRRa3Gq1gnrQh/ltHH19Eyi/WfE7cF8RYNSKmhlJsfy3o+nA1BS5WL8bz5mUwgNlhjWhV5EuOesEaz7zRwmZSZRUqVdLJVSbTOydwJf3DGTzORYAC779xIKiqssThWYsC70DS3ffoi9pdVWx1BKhbD+PWJYcPvJ/P3iLPaWVrN8xyGrIwWkxUIvIlEislRE8kRkjYjc559/r4jsFpGV/scZzWy/TURW+dfJae8PEIhl24pwew23njLEirdXSoWwiho3y7YVUVhajdvjxeM1LNp0gAiHjSkDelgdLyCBXAVQA8wyxpSLiBP4SkQ+8C/7qzHmLwG8xkxjzIE2pzwO1S4Pf/pgPSlxkVw7I9OKCEqpEFXt8nDmQ1+y7WDlUcu+P7k/yd0iLUjVei0WemOMAerOOjj9D9ORodrTna/nk7P9EL89dxQxEaF9dZtSqnPtLKpk28FKLpvSn4Ep3SirdmMMJEQ7uHBi8N46sLGAKp+I2IFcYBDwqDFmiYjMBW4SkR8AOcBtxpimGqwM8JGIGOCfxpgnmnmPecA8gP7922ekyUMVtby3ag8XZffjsinp7fKaSqmuo3diNAnRTt5ftZfXrp/KgJRuVkdqk4BOxhpjPMaYLKAvMElERgGPAwOBLGAP8EAzm59gjBkPzAVuFJEZzbzHE8aYbGNMdkpKSus+RTN2FFXi8hhmD+/ZLq+nlOpaYiMdPH7peIoqalm6NXTHy2pVrxtjTDGwEJhjjNnn3wF4gX8Bk5rZpsD/sxB4s7n1OkJynK/97MWlOzrrLZVSYSbePy5999jgHW++JYH0ukkRkUT/dDRwCrBeRNIarHYesLqJbWNFJK5uGjitqfU6Spr/rlILNuynTIcpVkq1Qan/+hunPXQvuAzkiD4NWCAi+cAy4GNjzHzgfn+3yXxgJnArgIj0FpH3/dum4uulkwcsBd4zxvyv3T9FM8pr3fXTwT5etFIqOI3tl0hCtJOnFm3DmJDph3KEQHrd5APjmph/eTPrFwBn+Ke3AJaNJrZ+TxkAP5iajtPeZa4NU0q1o9hIBzfNHMTv3l/Huj1ljOgdb3WkVgvr6ped3p3x/ROZn78n5AYhUkoFjxlDfB1ENuwrtThJ24R1obfZhD98dwxl1S5++95aq+MopUJUZnIsaQlR/Pfr7SHZfBPWhR5gaK84rj9pIG8s381XGy25OFcpFeIiHDZ+PHswK3YU1w99HkrCvtAD3DhzEJnJsdz95iqqaj1Wx1FKhaDvTehLcrdIXgjB7tpdotBHOe38/rzR7Ciq5P4P11sdRykVgpx2G6P6xLO2IPTa6btEoQeYOrAHV0zL4KlF21i0SZtwlFKtd7C8lsQYJ6Uhdl1Olyn0AP83ZxgDUmK5/dU8vQmJUqrVBvfsxpqCUrLu+4jfv7/O6jgB61KFPjrCzl8vzGJPSTVPfLHZ6jhKqRDz5wvG8sI1kxnRO56Xl+0MmR44XarQA4zpmwDAjqLQuAWYUip42G3CtEHJXDY5nZIqF/m7SqyOFJAuVeg37y/n759uBGB4WpzFaZRSoeq0kb0A+HTdPouTBKZL3InD4zW8nruLn72eXz9v9rBUCxMppUKZ2+sFwBsaLTddo9Cf9tfP2by/AoDx/RN58oqJJMaE7pCjSinr7C2pZtYDCwGYM6qXtWECFPaF3u3x1hf5F66dzLSByRYnUkqFstzth6is9XBRdj9G9UmwOk5Awr6N/kfPL6+fbm2Rf3TBJm57JS9kzqwrpTqW12tYsvUgACZ0bp0d3kf01S4PH6/1nSzJTI5t1bY3PJ/L+6v2AjC6TzwnDkmhxuXlH59vxmMMPWIjSIqNIDHaSVyUk7goB/HR/p/+53FRTuy20L1ZgVLqSGv3lPLfr7eT3C2C7PQkq+MELKwL/fur9tRPv3b91BbXN8bg8hi2HCivL/IA97575MiX6T1iKK50BXTRVUyEndhIB90iHcRG2omNqJt2+Oc3XO77WbfTiPfvNOKjnUQ6bIjoTkMpK327z3ePi/9eNTmkxqUP60L/9soCAEb3SaBHt8gm16lxeyipdFFYVsNZD391xLL//eRErns2l+0HKzlzdBpxUQ5uOWUwaQnRALg8Xsqq3ZRVuyirdlNa5aK04fNqF+XVbipq3ZTXeKiocVNe42ZvaXX9dHmNm2qXt8XP4rRL/TcF307g8LeH+Gjft4d4/7eIup1EnH9ZfLSTuEiH7iiUOk7Lth0iLsrB0F6h1T07rAv9bacN4fNv97PtQAX/+WorZ4/tjcHQM853L9nKWjcjfvlhk9uO7ZvAsF7xfHzrSdht0mQTjNNuI8nfhHM83B4vFbWHdwRl1b4dRsMdR2nV4fm+5y72llbXL6tyHXtUTodN6B4bQVJMBN1jnfSIjaR7rNP/3PcZ4qOd9IqPYnha6BypKNWZ8nYWk9UvMeSaZMO60I/pm8jC20/m52+t4jfz1/Kb+b4mmMum9Cch2smjCw4Pg3Di4GTOGpPGnFFpbN5fzgh/sYtwdPz5aofdRkK0jQT/3ebbou7bRWnV4W8TdTuBkioXhyprOVRZS1GF77F+bymHKn3zG59rXnL3bFL9N1ZXSh22sbCMSZnpVsdotbAu9AAZybE8d/Vk8neVsGxbEb99bx3PfXN4POkzR6fx0CXjjthDj+/f3Yqox6Wt3y48XkNplYt9ZdXM+duX9IyLpGdc081cSnV1o/skMD+/gFtPGUJCTNsPzDpb2HevBBARxvZL5JoTB3DrKUNIio3gw5/MYOsfzuDRS8eH3New9mT3N+kM6xXPuVm9Kfafr6izp6SKihq3hQmVCh6/PmcURRW1IXdfiy5R6Bu65ZTBLL/nVIb2itOTk424vIZaj5ddh3wDvn22fh9T//AZVz29TK8lUAoY1SeB88b15YWlO9jo74ETCrpcoVdNKyyt5r38PZw+MpVx/RJ5Y/kurv1vLgBLthbxXoOuqkp1Va/n7uKdvN0MSI4luZmefMFIC70CIM8/3Oq8GQN4avE2fvpKHpMzk8j71WmMSIvnd++t0/vtqi7L6zX85cMN3PZqHpMyk3jjhhPofpy97RpbvbuEytqOaSbVQq8AX68dgHveWsNv5q9lzshePHnFRBKindx79kj2lFTzry+3WJxSqc5X7fJw80sreGTBJi6e2I+nr5x0XD3kGqtxe/i/1/I56+GvOP/xr9vtdRsK+143KjAzh/Yk0mFj7Z5SLpnUj9+eO7r+JPWkzCTOGN2Lxxdu5qKJ/bTrpeoy9pfVcO1/c8jbVczdZwzj2hMHtOu5vbJqF1c9vYxl2w4BsG5Px9x4vMUjehGJEpGlIpInImtE5D7//HtFZLeIrPQ/zmhm+zkiskFENonIne39AVT7iI6w88K1k/ndeaP4/Xmjj+qJdOec4Xj8X1+V6go27C3j3EcXsX5vKY9fOoF5Mwa2a5GvrHVz1dPLWLGjmIcvGcfFE/uRGt8x7f6BNN3UALOMMWOBLGCOiEzxL/urMSbL/3i/8YYiYgceBeYCI4BLRGRE+0RX7W1CehKXTk5v8pe5f48Yrjwhg9eW72L17tC4fZpSbbVwQyHnP74Yl8fLq9dNa/dx5z1ew80vrCB3+yH+fvE4zhqTxpYDFQgd0xOwxUJvfMr9T53+R6B97SYBm4wxW4wxtcBLwDltSqosd+OsQSTFRPCb+Wu1u6UKW899s52rnl5Gv6QY3r7pBEb3bf8x53/73lo+XV/IfWeP5IzRvfj1/LUs3VpETKS93d8LAjwZKyJ2EVkJFAIfG2OW+BfdJCL5IvKkiDR1OWkfYGeD57v885p6j3kikiMiOfv37w/8E6hOEx/l5CenDmHJ1qL64Z+VCicPf7qRX7y1mpOH9uS166fWD2DYnp5fsp2nFm3jyhMyuHxqBo8t3MxTi7Zx/vi+/OeHE9v9/SDAQm+M8RhjsoC+wCQRGQU8DgzE15yzB3igiU2b+h7S5KGgMeYJY0y2MSY7JSUlkFjKApdM7Megnt34wwfrqXW3POqmUqFkfr7vepFffWcEsZHt31flmy0H+dXbazhpSAq/OHMEb6/czZ8/3MA5Wb358/fGtPq+GYFqVfdKY0wxsBCYY4zZ598BeIF/4WumaWwX0K/B875AQduiqmDgsNv4+ZnD2Xqggue+2W51HKXa1cPfH0dclIOrn8mhpLLl+020xp6SKm58fjn9e8Tw8PfHkbv9EHe8ms+kzCTu/94YbB04FEsgvW5SRCTRPx0NnAKsF5G0BqudB6xuYvNlwGARyRSRCOBi4J3jTq0sdfKQFE4cnMzfP91IcWWt1XGUajdDUuN44vJsth+s4O63VrXb69a4PVz/3HKqXR6euHwCB8pqmPdsDn2Tonni8glEOjqmbb5OIEf0acACEcnHV7g/NsbMB+4XkVX++TOBWwFEpLeIvA9gjHEDNwEfAuuAV4wxazrgc6hOJCLccPIgSqpcLNlaZHUcpdrV1IE9uGJaBu/l72m3A5l731lL3s5iHrhwLN1jIrjy6WXYRHjqiokkxrTvFbZNabERyhiTD4xrYv7lzaxfAJzR4Pn7wFFdL1V4iOuAdkylrFbj9hLltBEfdfxXwL68bAcvLt3Bj04eyOzhqVz67yXsKanmxWunkN6jY9rkG9MhEFSb1B3pdMbRiFKdLdJho9rl5bS/fUFRRduP6vN2FnPP22uYPiiZ208byu/fX8fSrUXcf/4YJqR33n0vtNCrNinyF/rusaFz8wWlAnXX3OHcNHMQmwrL+WRd27oSFxRXMe/ZHFK6RfLQJeN4N6+gvlvlueOa7GXeYbTQqzYp9vdI6K5H9CoM2WzC2Vm9AYh2tv5EaUmViyueWkpljYf/XJHN3pJq7nwjn0kZSdx9xvD2jtsibWBVbXKoopYop42oNvwRKBUKSqt8BzPxrRypstbt5fpnc9l6oIJnrpxEWnw033nkKxKinTxy6Tic9s4/vtZCr9rkUKVLj+ZVWCut9hf6qNaVyYc/28jXWw7y4IVjyc5I4oqnlrKnpIqX5k2lZ5w1I79qoVdtUlxZy56SavJ3FWMTwW47/HDabNjtgtMmOO02Ihw2nHYbTrvo7RtVyCir9t0EZEdRJeP6B3bidPXuEh5buJnvju/Dd8f35RdvrWLx5oM8cMHYTj352pgWetUmbq9vJIuzH1nUqu0i6gu/1O8AIhy2BvN90876eeJ7Xrde43Ub7ETsNsFhE+w2m/+nHP5pb3q+025jaK84S75Oq+BWN1T3twHeG7bW7eWO1/JJio3gl2eN4H+r9/LcNzuYN2MA50/o25FRW6SFXrXJn783hjUFpXi8Bo8xeP0/PV6D22Nwe724PAaXx4vL46XW7aXWY6h1H35e97PG48Xl9lLbYN3KKk+T69Y2+NleA2hOzkziuWsma7FXRzhzdBr3xKxm2dZDGGNa/Db6+MLNrNtTyhOXT6DK5eHON/IZ0zeB208b2kmJm6eFXrVJz/goelp8pym3x7czqXV7cXu9vp2M1zT46cXt3/Ecucxbv0Nat7eU+/+3gT9+sJ57ztJbJajDRITLp6Tz0GebOP/xxdxz1gjG9k3kl++sJi0hmhtnDqpfd/3eUh5ZsJGzx/Zm9vBULvv3EmrdXv5+8TgiHNYfQGihVyHLYbfhsPvujtVWM4f1ZF9JNf/5aitj+yVy9tje7ZhQhbrrThpIpNPOPz/fzHmPLWZASixb9lcAcPHEfvToFokxhl++vYa4KN/9lf/79Ta+3nKQ+8/vuNEoW8v6XY1SFvv5mSPITu/O/72Wz4a9gbXHqq4hNtLBjTMHsfiu2VwxLaO+yAPsL68B4MM1e1m6tYjbThuCy+PlgY++5aQhKVyQbW27fENa6FWXF+Gw8dil4+kW5eD653Lru9UpVadbpIN7zx7J6z+ayt8uygLg/VV7qXF7+P376xmaGsdF2f349fy1uDxefn3OyKDqYaaFXil85xweu3Q8O4sq+enLeXi9eqtEdbQJ6UmcPbY3fRKjWbLlIM8s3saOokp+cdZwFm8+yHv5e7hx5qBOG6wsUFrolfKbmJHEz88czifr9vHYwk1Wx1FBoqC4ihL/VbIrdhxi4M/fZ3dxFaeOSOXhTzcxa1hPJmYk8cu3V5OZHMt1Jw2wOPHR9GSsUg1cMS2DlTuLeeDjbxndN5GThuhtLbui3O1FrCkoJWfbId7NL6BbhIM7zxhGebUbY+DEwcls3l9BlcvD3WcM519fbGHbwUqeu3pyh99EpC200CvVgIjwh++OZsPeMm55aQXv3jSdfkkxVsdSnaja5eH8x7+uf37BhL6s2l3Cz988fBO9X5w5grl//4IfTM2ge4yTf3y+mdNHpjJ9cLIVkVukTTdKNRIT4eAfl03A4zVc/1wu1S6P1ZFUJ4py2vm/OcPqnx8or+F3543m1eunctqIVK49MZMHP95At0gHt8wezMOfbaLK5eGO04cd41WtpYVeqSZkJMfyt4uyWFNQyi/eWo1pr8twVVAxxlBS5WJTYTmrd5fUt8X/6OSBfHrbSfx41iDyd5Vw/uOLeWnpTv52cRaXTUnno7X7+OG0DMpr3Dy/ZDsXTezHoJ7dLP40zdOmG6WaMXt4Kj+ePZiHPt1I78RofnrqEKsjqeNgjGFnURW5O4rI3X6I3O3FbNlfTo3be8R6/ZNiuHPuMOaM7MVPTxvKdScN5PGFm3lkwSainL6xlRw24bIp6fzxg/XYbcIts4P7d0MLvVLH8BN/oX/o040kxTi54oRMqyOpANW6vazaXULu9sOF/YD/IqdukQ7G9U9k+qB0UuOjSImLJMJuY3tRJW+vLOCG55fTPymGiyf146Lsflw1PZNHFmzCawyv5uzkrDG9EeCdvAKunJZBrwRrhwNpiRZ6pY7BZhO+uGMmM/68gAc//pYzx/QmJS7S6ljqGAqKq3jum+28tGxn/f1e+yfFMGNwMuPTuzMhvTtDUuPqR6ds7Orpmby/ag8vLNnB/f/bwAer9vL0lRMB+Gx9IRW1Hq46IZM3VuzG4zV8f3L/TvtsbaWFXqkW9O8Rwwe3nMh5jy3ixueX8/y1OtJlMNqyv5w/f7iBj9buwxjDKcNTOW9cHyZkdG/VDT+cdhvnZPXhnKw+3P3mKt7NKyAxJoJhveJYv7eM9B4xjO6bwJ1v5JPVL5EBKcHbNl9Hf1uVCsDwtHj+dP4Ylm4r4g/vr7c6jmpg0aYDnPfYImY/+DlfbTrANSdm8vkdM3niB9nMHZ12XHd1mjE4mbJqNx+u2ctV/ma77Qcr2XqggjUFpZw1Jq29PkaH0iN6pQJ0TlYfVuwo5slFW5mU2Z05o0LjjzxcVbs8/PGD9Ty9eBvpPWL48azBXDKpf7u2l586ohdpCVG8vXI39549sn7+J2v3ATBnVK92e6+OpIVeqVa4+4zhrNhZzB2v5TM8LT7oxjTpKlbvLuGWl1aweX8FV0zL4M65wzrkRvV2mzBzWE/eXL6bGKeDd246gYLiKp5ZvJ2BKbH07R4aF9Np041SrRDhsPHIJeMQ4MYXluvFVBb4auMBLvjH11TUeHju6snce/bIDinydS6fkk6tx8stL6/AJkLu9kN8veUg35vQr8Pes721WOhFJEpElopInoisEZH7Gi2/XUSMiDR57a+IbBORVSKyUkRy2iu4UlbplxTDAxdmsXp3Kb97b53VcbqUBesLuerpZaT3iOHdm6d3ypADw9Piue/skXy58QBnPfwV//pyKz+Yms41J4ZOV9tAmm5qgFnGmHIRcQJficgHxphvRKQfcCqwo4XXmGmMOXC8YZUKFqf6L4X/15dbmTwgibPG6J2pOtona/dxw/PLGdKrG89eNZnusRGd9t6XTUnn5KEp5O0soU/3aLL6JXbae7eHFgu98V37Xe5/6vQ/6q4H/yvwM+DtDkmnVBD72Zxh5Gw/xJ2vr2JU7wQyguS2ceHowzV7uemF5QxPi+fZqyaTEOPs9Ax9u8eETJt8YwG10YuIXURWAoXAx8aYJSJyNrDbGJPXwuYG+EhEckVk3jHeY56I5IhIzv79+wPNr5RlnHYbD18yDrtNtL2+g9S6vfz7yy3c+PxyRvZO4NmrrSnyoS6gQm+M8RhjsoC+wCQRGQP8HPhlAJufYIwZD8wFbhSRGc28xxPGmGxjTHZKio4BrkJD3+4xPHDBWNYUaHt9e1u+4xBz/v4Fv31vHScOTubZqyeREK1Fvi1a1evGGFMMLATOATKBPBHZhm8HsFxEjupUaowp8P8sBN4EJh1XYqWCzCkjUpk3YwDPfrOdd/MKrI4T8jxew6MLNnHhP76m1u3lySuyefKKicRFaZFvqxbb6EUkBXAZY4pFJBo4BfiTMaZng3W2AdmNT7iKSCxgM8aU+adPA37dnh9AqWBwx+lDydlWxF1vrGJUnwQytb2+TfaWVHPLSytYsrWIM8ek8ftzR2tTTTsI5Ig+DVggIvnAMnxt9PObW1lEeovI+/6nqfh66eQBS4H3jDH/O97QSgUbp93Gw98fj8Mu3Pi8tte3xdqCUs59dBGrdpfwlwvG8sgl47TItxMJxhsqZGdnm5wc7XKvQs+n6/Zx9TM5XDq5P787b7TVcULGgvWF3PTCcuKinDx5xURG9I63OlLIEZFcY0x2U8v0ylil2tHs4alcN2MAzy/ZwTvaXh+QZ7/ZztXPLCMjOZa3bjxBi3wH0EKvVDu7/fShTEjvzl2v57P1QIXVcYLaows2cc9bqzl5aE9euW5q0N/AI1RpoVeqndX1r3c6bNyg7fXNevbrbfz5ww2ck9WbJy6fQGykjrHYUbTQK9UBeidG89cLs1i3p5Rfz19rdZyg89XGA9z77lpmD+vJgxdm4dAbuXQo/ddVqoPMHNaT608ayAtLdvD2yt1WxwkaW/aXc8PzuQxK6cbf/VcWq46lhV6pDnTbaUPITu/O3W+sYsv+8pY3CHMlVS6ueSYHh93Gv3+YTTdtrukUWuiV6kC+/vXjiND2ejxew+2v5rGjqJLHLx1Pv6TQHCAsFGmhV6qDpSVE8+BFWazfW8Z973bN9npjDL94axUfr93HXWcMZ/KAHlZH6lK00CvVCWYO7cmPTh7Ii0u7Znv9O3kFvLh0J9efNJCrp4fODTvChRZ6pTrJbacebq/f3IXa64sra7nv3bVk9UvkjtOHWh2nS9JCr1Qncfjb6yOd9i4zHk61y8Ndb6yiuLKWP3x3tPawsYgWeqU6UVpCNA9eONbfXr/G6jgd7r531/DB6r3cNXc4w9N0aAOraKFXqpOdPLQnN5w8kBeX7uStFeHbXr/9YAWv5Ozih1PTuXbGAKvjdGla6JWywE9PHcKkjCTufnMVmwrDs73+oU834bAJN84cZHWULk8LvVIWcNhtPHTJOKL87fVVteHVXl9YWs1bK3dz6eR0esbrQGVW00KvlEV6JUTx14uy2LAv/NrrX83dhcdr+P7kflZHUWihV8pSJw1J4caZA3lp2U7eXLHL6jjtotrl4Z+fb+bkoSkM6hlndRyFFnqlLHfrKUOYlJnE3W+sZlNhmdVxjttn6wsprXbzw6kZVkdRflrolbKYwz9+fUyEnRufXxHS7fW7i6u48/V8hqbGMXWgDnMQLLTQKxUEUuN97fXfFpbxq3dWWx2nzf7z5VYqaz088YMJRDntVsdRflrolQoSM4akcNPMQbySs4vXc0Ovvd7t8fLGil3MHZ1Geo9Yq+OoBrTQKxVEbpk9mMmZSfzirdBrr1+xs5jiShdzR/WyOopqRAu9UkGkrn99bKSdG55fTmWt2+pIAft0XSEOmzB9cLLVUVQjWuiVCjKp8VH87aJxbCws55dvh07/+s/W72NSZhLxUU6ro6hGtNArFYSmD07m5lmDeS13F6/m7LQ6Tot2FlXy7b5yZg3raXUU1QQt9EoFqVtmD2bqgB7c8/ZqNuwN7vb6z9YXAmihD1ItFnoRiRKRpSKSJyJrROS+RstvFxEjIk02zInIHBHZICKbROTO9gquVLiz24S/X5JFt0gnNzyfS0VN8LbXf7R2L4N6dmNASjero6gmBHJEXwPMMsaMBbKAOSIyBUBE+gGnAjua2lBE7MCjwFxgBHCJiIxoh9xKdQk946J46OIsth6o4BdvrcYYY3Wko7g8XpZvL2b6ID0JG6xaLPTGp24cVaf/Uffb9lfgZw2eNzYJ2GSM2WKMqQVeAs45vshKdS3TBiVzy+whvLliNy8vC772+nV7SqlyeZiQ3t3qKKoZAbXRi4hdRFYChcDHxpglInI2sNsYk3eMTfsADX8zd/nnNfUe80QkR0Ry9u/fH1h6pbqIm2YNYvqgZH71zhrW7Sm1Os4RcrYdAiA7Qwt9sAqo0BtjPMaYLKAvMElExgA/B37ZwqZN3SCyyaN/Y8wTxphsY0x2SkpKILGU6jLsNuFvF2eREO3kxueXUx5E7fW52w/RJzGatIRoq6OoZrSq140xphhYiK/5JRPIE5Ft+HYAy0Wk8SVxu4CGA1L3BQramFWpLi25WyQPXTKObQcruOuNVUHRXm+MIWd7kTbbBLlAet2kiEiifzoaOAVYYYzpaYzJMMZk4Cvo440xexttvgwYLCKZIhIBXAy8054fQKmuZMqAHvz01CG8m1fA80ua7APRqQpKqtlXWqPNNkEukCP6NGCBiOTjK9wfG2PmN7eyiPQWkfcBjDFu4CbgQ2Ad8IoxJnQu9VMqCN1w8iBmDEnh1/PXsnp3iaVZlmw5CMCYvomW5lDHJsHw9a+x7Oxsk5OTY3UMpYLWwfIaznzoKyKdNubfPJ04i4Yd+MGTS9myv5wvfzYTkaZOyanOIiK5xpjsppbplbFKhaAe3SJ5+Pvj2HWoijtft6a9/kB5DV9t3M85Wb21yAc5LfRKhaiJGUncftpQ3lu1x5L2+i++3Y/XwJyRaZ3+3qp1HFYHUEq13XUzBrB48wF+M38tkzOTGJx67JtxG2PYU1JN3s5iNhWWYwC311BR46bW7SU+2sHAlG5kpyfRv0fMMV9r6dYiEmOcjOwd346fSHUELfRKhTCbTXjgwrHM/duX/Pillbx5w7SjbuG3r7Sat1fuZunWQ+TtKmZ/Wc1RrxMTYSfCYaO82o3b62sGGtk7nrPG9GbejAHYbUc3zWw/WElGj1hsTSxTwUULvVIhrmdcFH++YAxXPZ3D/f/bwC+/4xtOKm9nMY8v3MzH6/bh8RoGpMRy4qBkxvZLZGy/RIb1isNhE2wi9cXa5fGy9UAFn2/Yz6u5O/nT/9bTOzGKc7KOvqDdYRdMbfB15lBH00KvVBiYNSyVK6Zl8OSirTjtwpqCUr7adID4KAfXnJjJRdn9AhpZ0mm3MSQ1jiGpcVw1PZNTHvycBz/+lumDkunRLfKIdRNjIth1qKqjPpJqR3oyVqkwcefcYQxNjeOfX2xh/d4y7po7jEV3zuKuucPbNHyw3Sbc/70x7Cmp5rzHFrNlf/kRyx02we31tld81YH0iF6pMBHltPPklRPJ2VbE6SN7HdVW3xYTM5J4ad4Uzn98MY8t3MxfLhhbv8xpF2pcWuhDgRZ6pcJIn8Ro+jTRnn48xvfvTnZ6d7YeqDhi/oZ95aQlRLXre6mOoU03SqkWTRnQg+U7DrF0axEAr+bsJG9nMeeOa9+diuoYekSvlGrRD6Zm8G5eARc/8TWp8VHsKalmUmYSl01JtzqaCoAWeqVUi1LiInn7pun858st7DxUxeg+CVw6pT9OuzYKhAIt9EqpgCREO/npaUOtjqHaQHfHSikV5rTQK6VUmNNCr5RSYU4LvVJKhTkt9EopFea00CulVJjTQq+UUmFOC71SSoU5seKmwi0Rkf3A9mOskgwc6KQ47SHU8kLoZQ61vBB6mUMtL4Re5uPJm26MSWlqQVAW+paISI4xJtvqHIEKtbwQeplDLS+EXuZQywuhl7mj8mrTjVJKhTkt9EopFeZCtdA/YXWAVgq1vBB6mUMtL4Re5lDLC6GXuUPyhmQbvVJKqcCF6hG9UkqpAGmhV0qpMBdShV5EskTkGxFZKSI5IjLJP3+Sf95KEckTkfOszgrHzHuqiOSKyCr/z1lWZ61zjMw9RGSBiJSLyCNW56zTXF7/srtEZJOIbBCR063M2ZCIvNzg93WbiKz0z48Qkaf8vxd5InKypUH9jpHXKSLP+POuE5G7LI4KHDPvpQ3mrxQRr4hkWZvWp7nM/mVjRORrEVnj/7du/R3ZjTEh8wA+Aub6p88AFvqnYwCHfzoNKKx7HqR5xwG9/dOjgN1WZw0gcywwHbgeeMTqnAHkHQHkAZFAJrAZsFudt4n8DwC/9E/fCDzln+4J5AI2qzMeI+/3gZf80zHANiDD6ozN5W00fzSwxep8AfwbO4B8YKz/eY+2/B6H1BE9YIB4/3QCUABgjKk0xrj986P86wWD5vKuMMYU+OevAaJEJNKCfE1pLnOFMeYroNqqYM1oMi9wDr4iVGOM2QpsAiY1sb1lRESAC4EX/bNGAJ8CGGMKgWIgaC72aSKvAWJFxAFEA7VAqUXxjtJE3oYuaWa+pZrIfBqQb4zJAzDGHDTGeFr7uqF2z9ifAB+KyF/wNTtNq1sgIpOBJ4F04PIGhd9KP6GZvA2cD6wwxtR0ZrBj+AktZw4mP6HpvH2Abxqst8s/L5icCOwzxmz0P88DzhGRl4B+wAT/z6UW5Wuscd7X8O1Q9+A7or/VGFNkVbgmNM7b0EX4sgebxpmHAEZEPgRS8B283N/aFw26Qi8inwC9mlj0c2A2vl+m10XkQuA/wCkAxpglwEgRGQ48IyIfGGM6/OizrXn9244E/oRvr91pjiezFdqYV5pYv9O+6R0rszHmbf9046PKJ4HhQA6+sZ4WA51ywNLGvJMAD9Ab6A58KSKfGGO2dGhY2py3btvJQKUxZnUHRjxKGzM78DWZTgQqgU9FJNcY82mr3tzq9qhWtl2VcLjvvwClzay3AMgO5rxAX+Bb4ASrc7bm3xi4guBqo28yL3AXcFeD9T4Eplqdt0EeB7AP6HuMdRYDI6zO2lxe4FF8357rnj8JXGh11pb+fYG/AndbnTHAf+OLgacbPL8HuKO1rx1qbfQFwEn+6VnARgARyfS3EyIi6cBQfCeGrNZc3kTgPXyFaJE10ZrVZOYg1lzed4CLRSRSRDKBwQRPEwj4vnWsN8bsqpshIjEiEuufPhVwG2PWWhWwkaPyAjuAWeITC0wB1luS7mhN5UVEbMAFwEuWpDq2pjJ/CIzx/2448P2ut/p3IuiablpwLfB3/weuBub5508H7hQRF+AFbjDGBMPQpM3lvQkYBNwjIvf4551mfCfgrNZcZkRkG74TnxEici6+zFYXoibzGmPWiMgr+P4o3MCNpg0nsTrQxRzdrNAT3/kGL7AbuLzTUzWvqbyPAk8Bq/F9m3rKGJPf2cGa0VRegBnALtMJzUttcFRmY8whEXkQWIav6fF9Y8x7rX1hHQJBKaXCXKg13SillGolLfRKKRXmtNArpVSY00KvlFJhTgu9UkqFOS30SikV5rTQK6VUmPt//Mitk7JKlW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bafedc65-1410-41d4-b324-8f5af9b95726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "491 -76.52474484837771 34.73385689822422 0 0\n",
      "723 -76.47694502529616 34.93616015001211 0 1\n",
      "725 -76.3667064760517 34.814679601488194 0 1\n",
      "726 -76.25937258621086 35.09389426483537 0 1\n",
      "2636 -76.3530511940651 34.8849748269376 0 1\n"
     ]
    }
   ],
   "source": [
    "#ok, which empty tract did I miss?\n",
    "pt1 = Point(-76.2,34.5)\n",
    "pt2 = Point(-76.2,35)\n",
    "pt3 = Point(-76.4,35)\n",
    "pt4 = Point(-76.4, 34.5)\n",
    "killPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].intersects(killPoly):\n",
    "        x = tractGeom[t].centroid.x\n",
    "        y = tractGeom[t].centroid.y\n",
    "        print(t,x,y,tractPop[t],isSkippedTract[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9d8fedbd-8472-4cb0-9d82-0e6f6e4e2c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#remove this errant tract\n",
    "isSkippedTract[491]=1\n",
    "tractMAP = tractMAP.difference(tractGeom[491])\n",
    "plotPoly(tractMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.8024432809773124 2865\n",
      "100 0.6619718309859155 497\n",
      "200 0.7626459143968871 1028\n",
      "300 0.751441753171857 3468\n",
      "400 0.4227053140096618 414\n",
      "500 0.44183168316831684 1616\n",
      "600 0.5508905852417303 786\n",
      "700 0.31746031746031744 189\n",
      "800 0.7609271523178808 3020\n",
      "900 0.28166107815321123 3877\n",
      "1000 0.222508038585209 1555\n",
      "1100 0.3837448559670782 1944\n",
      "1200 0.6934189406099518 1246\n",
      "1300 0.5982596084118926 1379\n",
      "1400 0.07443875541551792 2539\n",
      "1500 0.0 0\n",
      "1600 0.782716049382716 1620\n",
      "1700 0.75050641458474 2962\n",
      "1800 0.6972255729794934 1658\n",
      "1900 0.5809344030202926 2119\n",
      "2000 0.5759444872783346 1297\n",
      "2100 0.875 240\n",
      "2200 0.6683595733875064 1969\n",
      "2300 0.7559645535105658 1467\n",
      "2400 0.0 0\n",
      "2500 0.44339622641509435 530\n",
      "2600 0.37532133676092544 1167\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 13.852168871267956 11.335656805809036\n"
     ]
    },
    {
     "data": {
      "image/png": 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AaAucJSILRGSGiPSsYtcmwNYjXm/zvFdVGWNEZLGILM7NzT2pDxGs/vbGEvI3pxPVZB2bFnUgOiIMgJztTnCWERNpvX7o1YWsmDyILhfN4Lze6XaGrFTAcTqcNIttBkCIw7s90OzqZlmrRG+McRljugJNgV4i0hEIARKAPsB9wCcicmybQVVtCFV+PGPMa8aYTGNMZnKy7+Z09GeLlloNefNmRdEgKuzQ+81auKAikpy9hfS9djqP3d6ZiKZrmP6f3naFqlRAq5w4PNQR6vWyRoyADRsgO9vrRR1yUr1ujDF5wHRgOFbt/HNP085CwA0kHbPLNqDZEa+bAt79bRTgKioOTyS8J9f659mw5egrNyWem1gzmiYw/6PBRDdbx4LpCcTHRPgyVKWCRmWi93aNHmC4Z+SF777zelGH1KbXTbKIxHueRwJDgV+BL4EhnvfbAmHAsXOdLwLaiEi6iIQB1wBf11XwwWbjRsjIgORkuP562LuhGSSupTxp6VHbfTShGwN/Ox2AqBaryMtuT+fWKb4PWKkgkV+aD0Co0/s1+latoHVrP0v0QGPgJxFZjpW4vzfGTAbeAlqJyC9YF1l/Z4wxIpImIlMAjDEVwO3At8Bq4BNjzEpvfJBAZwxcfDFs3mwl+7ffhh2rWkHn97nq0yv5es3h78fmjeKY8c5glq3bzfqfmxPi1NshlDod2XuzCXOG0SLON50Zhg2zet6UlfmkOMTYOZFhNTIzM81iu6dN97GDByEmxnp+4ADExVnJv/+j9zDHPQEA81f/+7dSKhj0eaMPbuNm4U0LfVLel1/CpZdafeoHDqybY4pIljEms6p1WhX0E9HR0KkTdOwIDRrA/PnW4/6rBtsdmlJBb8XuFfRu4rvODGefDU6n75pvNNH7ke7draYbgF69oHdvuOiMi7i/3/0A/LDhBxujUyp4ZSRmsDFvo8/Ki4uDPn000ddLX30FBQWQl3f0+xefad1jdu5755JTkOP7wJQKct0bd2fO1jkUlftubIJhw2DxYti71/tlaaL3E3v2WAlexPq2P1K/Zv14+YKXAdhesN33wSkV5K5sfyV5JXks2LbAZ2UOG2Zdh/vxR++XpYneTxw8aC2ff95K9seKDIk8aqmUqjuVo1dGhVY9h4M3ZGZCfLxvmm800fuJ996zlsfW5is5xPqnKq4o9lFEStUfk1ZNokmDJvRsUtVILt4REgLnnGMlem93ftRE7weKiuDpp6FzZ/i//zt+/aSVk/jtl78FoGtqV98Gp1SQO1B6gGnrpnF5u8sPVah8Zdgw2LoV1qzxbjma6P3AF19Y7fPPPVd1s824aeMA+E2n3/jkFm2l6pPJaydT6irlyg5X+rzsYcOs5ZQp3i1HE70Nnn8e2rSBQYNg4kR4/HGIiLBeVyUuIo7WCa1579L3fBuoUvXApFWTSGuQRr9m/XxedsuW0LWrVdnzJq0e+tCuXdYdceOsCjrr1sHMmdbzP/+56to8QEFpAdsLtnP84KBKqdO1aPsihrYa6vNmm0rnnGNV+EpKrAqfN2iN3kdmzYLUVLjlFmjWDHJyYP9+OP98uPlmeOSR6vfNTMskPiIefxyuQqlA5zZuwp3htpV/1lnWmDeLFnmvDK3R+8hzz1nLjz+GSy6BMM/w8t98c+L9vlj9BV+t+QqAovIiosOivRekUvWQqXqKDJ8ZMMBazpplJX1v0ETvZV98Adu3W8tx4+Cqq2q33yuLX+HrNV8zdZ0159j4/uM1ySvlJVLlHEm+0bAhtG9vJXpv0UTvRZMmHU7sKSmH2+ZrUlJRwthvxgLQLLYZ826YR5PYKmdgVEqdJn9oEu3Rw7sThmui96KFnhFPV6ywJhuIquVNdy8sfAGwavFZOVk0ndCU/1z2H67rdJ2XIlUqeN097W6eXfAsMWExOMVJSnQKjRs0pnGM9dh1cJftHR1at4b334fSUmte2bqmid5LjIHZsyE93Rp6+GREh1pNNI/PefzQewu3L9REr9QpqEzig1sOJj0+nd0Hd5NTmENWTtahQQIrJwe3S3q6lTM2b4a2bev++JrovaCgAP7zH2s8+WeeOfn9b8m8hfzSfJ6e9zTvXfoeY78Zy66Du+o+UKXqgT/2/yMvLXqJ9Ph0nh/x/HHri8qLfDrGTVVatbKWGzZ4J9Fr98o6Nn48xMbC2LHQr1/t2+WPJCKMHzCe3PtyGZ4xnNjwWOZsmUNBaUHdB6xUkGsU04gRbUbw6apPq1xvd5KHoxO9N2iir0PZ2fDEE4dfjx8Pjjo4w5mNM9l6YCsLtvtuCFWlgklkSKRfJPTqpKZabfMbvTT3iSb6OlJaat3hBrByJVRUwIUX1s2xuzXuBsA3a2vodK+UqlJiZCI5hTk+nVjkZDgc1nAImzZ56fjeOWz9MnmydYfr1q3w7LNWn1ins+6Of36b8wHI3pdddwdVqh65vN3lFJUXcc+399gdSrUSEiA/3zvH1ouxp2nx4sM199deg5tuqvsyJsybAMD4AePr/uBK1QODWw4mNSaVV7NeJa1BGg8NesjukI4TG+u9RK81+pPgch09v6PbbY1d43TCr796J8kDvJr1KgBnvX0Wn6/+3DuFKBXERITvR30PwF+n/5U/fv9Hv7hR6kixsXDggHeOrYm+Gi6XdWHk44/hwQfh73+HDh0gLQ1efhmysuDzz63l66/DGWd4L5aJIyYeer7twDbvFaRUEOuY0pFPr7R63jw590nu/vZumyM6WoMG3kv02nRThZ07rfkct1czD/ettx5+npBgDVLmTTdn3syoLqOI/kc0eSV53i1MqSB2efvL+e+1/+XCDy/klcWvMLrraL+Ztc2YuumlVxWt0R+juBiuuMJK8oMGwbvvwu7dsGUL5ObC0qVw113w1lvw2GMwd66V7L1tb5HVZuQ2bu8XplQQG9l2JL+M/YVSVynTN023O5xDioshMtI7x66xRi8iEcBMINyz/afGmL+KyMPATUCuZ9M/GWOOmxBLRDYBBYALqDDGZNZN6HXLGKsm/9BDMGeO1WRT1UiTSUkwYYLv46tsmx/ccrDvC1cqCCzbuYwwZxitE1vz655fAejSqIvNUR22a5eVX7yhNk03pcAQY0yhiIQCs0VkqmfdBGPMv2pxjLONMXtOOUofuPZaK7kDjB5d++GEfaG4vJh/zP4HA5oP0ESv1Cl4adFL3DblNgCc4sRlXKTHp9syfWB11q07fC9OXasx0Rvr0nSh52Wo5+Ffl6tP0/TpVpK/4gqr58y559od0dHGTRvH7oO7eeuit+wORamANGvLLGLCYnj5gpdZs2cNFe4Kft/t94SH2Dez1JGKimDbNmsUS2+o1cVYEXECWUAG8KIxZoGIjABuF5HfAouBPxhj9lexuwG+ExEDvGqMea2aMsYAYwCaN29+8p/kNEycaI0X/+673msjOx2T106mV5NeXND2ArtDUSogdUzuyEe/fIRTnDw25DG7wznOLs+YhU2beuf4tboYa4xxGWO6Ak2BXiLSEXgZaA10BXKAp6vZvb8xpjswArhNRAZWU8ZrxphMY0xmcnLyyX2KU1ReDk89ZXWTHDHCP5O827jZWbiTYa2G2R2KUgHr3n73EhcexzfZ/jmMSGW3yrg47xz/pHrdGGPygOnAcGPMLs8XgBt4HehVzT47PMvdwBfVbWeHZs3g/vuha1f44x/tjqZqgmAwTFo1ye5QlApYYc4wyt3lNIpuZHcoVapM9LGx3jl+jYleRJJFJN7zPBIYCvwqIo2P2OxS4Jcq9o0WkQaVz4FhVW1nh8cfP/xzacYMaNfO3niqUzlpwpq9azhQ6qW7KZQKcgZDuavc9onAq2N7ogcaAz+JyHJgEfC9MWYy8KSIrPC8fzZwN4CIpIlIZTfLRli9dJYBC4FvjDHT6vxTnIIHHrCWWVneO7l1JdxpXTCKCYuxORKlApNDHAxqOYgvfv0Cl9tldzjHqRzjxltNN7XpdbMc6FbF+6Oq2X4HcL7n+QbAfzqqekzytIL861/Qvbu9sdRGeEg4mWmZOETvb1PqVI3pPoarPr2KWVtm+V03ZX+o0QedTz6xlt4ahKyu3djtRuZvm8/6fevtDkWpgNUl1apzbsrbZG8gVdBE7wX9PPdIlJbaG0dt3dvvXkKdofxz9j/tDkWpgNUqoRWNohvx1Zqv7A7lOAcOWOPcRHlpEqx6megr+6ru2GFvHLXVuEFjxnQfwzvL3vHL2ohSgSDEEcKozqOYvHYyuw/utjuco+TlWbV5T9+LOlfvEr0xMG2a9e3pi8HI6sr9/e/HIQ4en/243aEoFbBGdx1Nhbui2onC7bJxozWVoLfUu0S/ZIk18uTFF4OPb8A9LU1im3BDtxt4a8lbbM3fanc4SgWkdsntiAiJODSomb/YuBFatfLe8etdog8NtZZ1NXG3L1VOJfjEnCdsjkSpwFRQWkBJRQmRIZF+M8OUMdak4Fqjr0OVk3bX5eTdvtI8rjmju47m9Z9fZ/uBamZFUUpVKyo0isYxjXly7pOc9fZZVLgr7A6J3FxrLHpN9HUoORnCw2HWLLsjOTXjB4ynzFXGh798aHcoSgWcUGcoK8au4I5edzBn6xyW5CyxOyQ2b7aWLVp4r4x6l+jLy61loHStPFbL+JaEO8O1941Sp6hhVEPu738/APO2zbM5GqvZBrRGf0oWLID337favyq5XNb0f6Wlh4dACCRlrjKeX/A8pa5S4iPi7Q5HqYDVNLYp8RHxrNmzxu5Q2OrpW9GsmffKCMrJwXNyYMgQazD/1FQYOtQauOyOO2DFCrjtNv8dxKw6+4v30++tfod6C1zYNgCvJivlJ/YX7yevJI/IUPvHJt+zB0JCID7ee2UEXaI3BsaOtS5uwPGzRd1xBzz3nO/jOl2JTyYeer78luV0atTJxmiUCmxXf3o1AEPSh9gciXUxtmFD790sBUHYdDNuHHz1lTUM8TPPWCewcsiDe+6xBjLz5gn1htKKwxcUdv5hpyZ5pU7Tgu0LaBHXghEZI+wOhVWroE0b75YRdIl+4kRred99cPfd1s+iOXOsmv7TT0NYmL3xnYqx34wFoFF0IxrFnHjihOyt+2jccwFfzMj2RWhKBZx1+9ZxoPQADcIbHJrvwS4uFyxdCj16eLecoGu6CQ+H228PvFr7iby99G2g5nZ5t9vQ67zN5K3uze9uXA5vZPPF9zl8Py2cho1KaZzmpnEjB40bhdG0USQt0qJp3SSO1k0SiAgLuj8Fpar0xGzrhsOh6UNtjgTWrLGuJWqiPwnFxVaPmoYN7Y6k7hw5+NI/zvlHtduVlbv421tZ5K22ZmosWNeZywYDWL8Jd0XtZWXRCU5MRD4hUQcIjS4kokExUQ1KiYktJzbeTUICJDV0kNIwhKapkbRsYn1BZDRNICYyAH8iqXptY95GMtMymTB8gs/Lzs6GN9+EAQNg5EiYOtV6v2dP75YbVIl+/35rmZh44u0CyTdrD09mnBSVdNQ6t9vw89qdPP76Wj57ZhCV0/H2uGw6WZ8PBiDziul8+2ZvEmMbkldYQvbW/WzYfoAtOQfZtquEnF3l7M415O0XCvKdFOaHUVwYQe7mOHIOxuAujoOKiOoDjMgnJCaPiNgDRMUVE5tQSkJDF0lJ0CjZSdPUcJo1jiQ9rQFtmyfQLCUWhyOIfm6pgLN813Kf91orLYVHH4UnnrCaa555Bm64Ad5+2+oheOaZ3i0/qBL9vn3WMpgS/dUdr+bvs/7OzsKdfLLyEwa2GEhydDIhjhBSumaxd0Um1myPFkdcDgsnDeJAUQnxMRHA4EPr4mMi6NmuMT3bNT6unBPZd6CYTTvz2ZxTwOacg2zNKWbHznJ273Gzd4+Qty+EgrwICvY0YO/GpqwrTKz+yyG0iJC4XKIS84hLOkhKahmN06Bl0zCSG4Zy+1UdSYy1v8ubCk57i/aSW5RL++T2Pitz2TL47W9h+XL4/e+t64fjxsErr8CgQYcnQvKmoEz0gTT8cE2iQqOYMXoGF310Edd8dg24hQ7uUWS/8RBlOzMBCE9dx3MvFRMdGcIVQ9rgcIgnydeNxNhIEmMj6d42tVbbu92GPfkHWXvo10MR23JKyNlVwY4cyN0ZSl5uNDuz09i6KAXKD8+28PHnM1k5ZWCdxa7UkTbs3wBAekK618sqKICHH7Y6iDRsCP/9r9VcA/Ddd1BW5rvOIUGV6PPyrGUwJXqwhiied/18LvjNZn74JIOVR6zLWrOT7m0zbIutKg6HkJIQTUpCNAM6n3hbt9uwLTefGx5cwg9vDKaz9hxVXpSekE5ESATfrP2Gy9pd5rVyFi2Ca6+FDRvguuuse3eOvXboyx6AQdW9sjLRe/MOM7tM/SaUHz45nND/+e8sjKHWtWx/5XAIzRvF8d4TmRCRT1aW1X5fVu5izvJtNkengk1SVBI3dLuB95a/57V5HV57Dfr3t2rsM2daQ7HY3UEkqBJ95cXYYEv0u3fDJZdYzzt2hIoKGP87L/fH8rHNOQegIpwD+8Nwuw3NemUxoEtTPv1prd2hqSBzZ+87KXeX8+KiF+v0uG63de/OzTfDOedY/eMHDKjTIk5ZUCX6yhp9XJytYdSJ4mKYMAGuvx4aHXGP1M8/B+ZY+jX568S1UBHBy88kMmzMDHYvtXoQjX/Mv+b2VIHNbdw8+L8HAchMy6yz41ZUWL1onn0W7rwTJk/2r04hQdVGv38/NGhgDRAUqCoqrC5XjzwC24+YWyQjA1avDuzPdiK7dgqE5zN3yR5+fHMw6YNmE9PAxYqpfVm+fjedW6fYHaIKcMYY7pp2F5NWTeKpc5/iivZX1MlxKyrgmmvgs8+si68PPeR/N2wGXY0+UJtt3G74+GNo3x7GjLGGLL3rLmtdRASsXBm8SR6s/yyUxvGve3oR02oFP0/O5Om/tABXCHc8usru8FQQeGLOE0xcOJF7+tzDvf3urbPj/u1vVpJ/5hn461/9L8lDECb6QOtxYwxMmWLdAn3NNdYQDl99BXPnWk03W7dat0gH4hg9J2PU1Q2sJ8bJvO9TiY+J4NxeLWnUfRGzPutAXmGJvQGqgPbvpf/mgR8f4LpO1/HUsKfq7Ljz5lmJ/re/tdrn/VWNiV5EIkRkoYgsE5GVIvKI5/2HRWS7iCz1PM6vZv/hIrJGRNaJyPi6/gBH2r8/sGr0s2fDwIFwwQWQnw/vvWddwLnoosO1gqZN/bOGUNfu/U03Lrt7BlPmbaBjq+RD7993TxjmYDL3/GuRjdGpQDY1eyo3fn0jQ1sN5e2L38YhdVO/LSiAUaOsX9+Vgyn6LWPMCR+AADGe56HAAqAP8DBwbw37OoH1QCsgDFgGtK+pzB49ephT0bmzMRdddEq7+tSSJcacf74xYExqqjEvvWRMaandUfknl8ttItLWmIgmvxqXy213OCrAzN8630T9Pcp0f7W7OVByoM6OW1ZmzOjRxjgcxsycWWeHPS3AYlNNTq2x1ddzgELPy1DPw1S/x1F6AeuMMRsAROQj4GLAK42u+/dDt27eOPLh4+/YAQcPnvhRVHTi9dnZVhPT449bE6FERdVcdn3lcAhX37CLdx47i4mfLGXcNV3tDkkFiLV713LBBxeQGpPKlOum0CC8QZ0cd9cu6N3bmtT7gQfgrLPq5LBeJcbUnLNFxAlkARnAi8aYP4rIw8Bo4ACwGPiDMWb/MftdAQw3xtzoeT0K6G2Mub2KMsYAYwCaN2/eY3Pl1OgnITHRagIJDbWaO0TA4Tj8vKpHVevh6LlmQ0KsLo1bttQcg4iVuKOjq3+0a2d1wQqkZiY77TtQTFLjIhp3zGb7gj52h6MCQElFCT1f78muwl3MvWEuGYl1c/e4MXDppTBtGrzzDlx5pZVD/IGIZBljquwzWqt+HMYYF9BVROKBL0SkI/Ay8BhW7f4x4Gng+mPLrupw1ZTxGvAaQGZmZm1/MRzlhResgYOsRpGjH2531e9Xta4y2YtYr10uKC+3eoZkZlqzwVSXxCMj60ebui8lxkbS+6IFzP/4LOYs30b/zk3tDkn5uey92fyy+xduzby1zpI8wIcfWp0lnnoKrr66zg7rdbWq0R+1g8hfgYPGmH8d8V5LYLIxpuMx2/YFHjbGnOd5/QCAMeafJyojMzPTLF68+KTiUsFt3i/b6delEb2umM2CjwfbHY4KAKO+GMVHv3zEghsX0L1x99M+3s6d0KEDtG1rdaTwtxsXT1Sjr02vm2RPTR4RiQSGAr+KyJFj3V4K/FLF7ouANiKSLiJhwDXA1ycZv1L07diEJr0Wsei/XdiTX2R3OCoAPDf8OZKikrhj6h2cbIX2WMbArbda19jeftv/knxNatO61Bj4SUSWYyXu740xk4EnRWSF5/2zgbsBRCRNRKYAGGMqgNuBb4HVwCfGmJVVFaJUTX5znWCKE/j8p/V2h6ICQGJkIuN6j2Pu1rnsKNhxWsf65BP44gtr8hBvTxLiDbXpdbMcOK4vizFmVDXb7wDOP+L1FGDKacSoFABul1UraxgfbnMkKlCUVJQgyHGzs52Mffuseah79YJ77qnD4HzIT64XK1WzPfsrAEhLirY5EhUoGkY2xGAY+eHIU26++dOfrK7Vr78euMOQaKJXAWN/nguAtKQYmyNRgeK2Xrdx8RkX88OGH06p+WbRImt8+TvugM41TKLjzzTRq4CRf8BaNm6oiV7VjkMcDGs9DIBQZ+hJ7etywdixkJpqjSYbyAL0h4iqjw7kA2EFhIXWzR2Oqn4oLLNu7I8JO7kKwmuvQVYWfPABxMZ6IzLf0Rq9ChgFB5w4Igtr3lCpIxSWFSIIkSGRtd5n1y6rbX7IEGtU2UCnNXoVMIoOhuDOb8z701YTFuogPNRJVEQIkREhREWEkBATQVxMOPExETgcenuyspS7yjEYKtwVtW6+uecea8yql14KjjvdNdGrwOHpNDFqRLuatw0pQUJKkNBSHGGlOELLcYaVERJWTkhYBaFhFYRGuAgLdxEe7iYswhARYYiIsIaxiIwQwsMhLFQIC3MQFipEhDsIDzv8iIxwEh7qICLcSc/2KUcNr6z8h/H84azdu5YOKR1q3P5//7Oaax56CM44w9vR+YYmehUwvv0wg09/WERFhaGswk1ZmZvSMkNJqZuSUkNJiaGo2FBSYs25W1oilJYIZaUOSkudlJc6KS8NoaIshJLCSNxlobjKw3CXh2HKIjAV4VBR+5/3Rwkr5Ic5mzkns0Xdfmh12kZkjOCJOU+weMfiGhO92w333QctWlgjUwYLTfQqYHRIT6bDTd6tNVe43BQWl7H/QAkHS8opLq049CgqqaCk1EVJmYuSUhfFpS5Ky9zkFZTz5H3tufbG3ez8ubk2G/mZfs36EeoIZfRXo4mLiCMpKon7v7+fSVdOoklsk6O2/fRT+PlnePddawrPYHHSg5r5gg5qpgLNJeOm89Xzgxk/cQH/vL233eGoY8zcPJOx34xlVe7hqTD+dvbf+PPAPx96XVFhzdkcHm7N9BZo49mc1qBmSqmaffTUACLS1vLUQ03IzdNB1/zNwBYDWXrzUm7odsOh9/YW7z1qm08/tSYFevTRwEvyNdFEr1QdiAgL4alni3Htb8olty20OxxVhVBnKK9f+DqfX/U56fHpzNoy69CwCMZYM761awcXX2xzoF6giV6pOnL7lV1oNXg2cz/ux7T5G+0OR1VBRLi03aWc1/o8lu5cSoXbGj/p229h2TK4/37/mTGqLgXhR1LKPl++2RbcIYzom47b7X/Xv+qrMlcZW/KtuUDv/e5eXsl6hZFtRx7qV//449C0KVx3nZ1Reo/2ulGqDnVqlULjVnvIWZ/EFTev4fPXA3Dw8gBU5ipjSc4S1u5dS05hDvER8SRHJeM2bjblbeKtpW+xKncV9/W7j0mrJgHw4FkPArBwIcyYARMmQFiYnZ/CezTRK1XHslfEExMFX0+KY9cTB2mUqMMq1zVjDNn7spmSPYWp66Yyc/NMSipKqt2+eVxzOqV04qm5TwFwc4+b6ZHWA7Dufo2JgRtuqHb3gKeJXqk6Fh0ZwiufL+eWyzpz8dgZzP94kN0hBY2cghzeXPImH6z4gNV7VgNwZtKZ3NzjZga2GEjHlI40jmlMfmk+uQdzcRkX0aHRtG3YFqfDyYxNM7j727sZ2XYkYE0q8vHH8PvfQ4MgHitP+9Er5SVth84k+6e+TJ61lQv6tbI7nIC2KW8TT855kreWvEWpq5SBLQZyVfurOL/N+aQnpB/arqwMtmyx5nY980yrT/yJTJhgjWuzbFlgjzcPJ+5Hr4leKS9ZtSmXDu1CiW7+KwdW99Y7Zk/RuKnjeH7h89bdrV1Hc3//+8lIzACgpMRqX588GaZNg/Xrra6SAKGhMGYMPP101QnfGOvLICkJ5szx4QfyEr1hSikbtG+ZTN/ffc3BtX3oe88Eu8MJSAfLDvL8wucB2DBuA69d+BoxZRm8+SZceqmVpIcPhzfftJL2Qw/B22/DRx/B734HL74IbdvCW29ZE4kcaf58WLsWbr7Zhg/mY9pGr5QX/fj8NUR9ncXCt65mx4MFpCUFcUOwF1ROGvLS+S8RVtKUW8dbE4K4XNCsmZXMR46EwYOtUUePdPXV1ljyDzxw+ELr9dcfXv/dd9YQxCNH+uaz2Elr9Ep5UWRYGG+8Gg4FTbjoliy7wwk4hWWFYGDWRz3JyLAm6B47FpYvh82brRr7iBHHJ/lK55wDCxZY3SZXrz563fffQ2YmJCZ6/3PYTRO9Ul52w4UdOePcmWR9MYDJc9bbHU5AmfjRKnhxFR8+k8mgQbByJUycCJ061X5CEBHo2ROmTj38XkGB9QUwdKh34vY3muiV8oHPXm0HoQe5/rZ9docSMO58ai7P3XkeYc5wnn05n6+/ttrbT8Xll1tfEjt2WK8XLrRGqxxUT3q+aqJXygc6pCcz8qYl5C7ryaNvLLI7HL932d0zmHh/P+IyVrF2aUPG3RJ3WlP69e9vLf/3P2s5d6617NXr9OIMFNq9UikfKSwuI7HFdsThZv+m5kRF1G7+0vrm1n/O4eU/9adxz/ms+l9X4mNOfwYQt9v6NRAaCk89Zd0gdeaZMGtWHQTsJ06re6WIRIjIQhFZJiIrReSRY9bfKyJGRJKq2X+TiKwQkaUiotlb1VsxkWH88ZFcyna1ZtSf5todjl/6+Ic1vPxQd+LOWMraGd3rJMmDNSLlyy/Dhg1w4YUQEgJvvFEnhw4INdboRUSAaGNMoYiEArOBccaY+SLSDHgDOBPoYYzZU8X+m4DMqtZVR2v0Kli53YbkLj+zf30r1qwxtGlWD7p81NLGHXm07ZyPuyKM5UtC6JBe99NGbtxoTRV49tnB19vmtGr0xlLoeRnqeVR+O0wA7j/itVLqBBwO4a0X4zClDbj81hV2h+M3Klxu+oxcS0VeY17+9x6vJHmA9HTrwmywJfma1OpirIg4RWQpsBv43hizQEQuArYbY5bVsLsBvhORLBEZc4IyxojIYhFZnJubW9v4lQo4Fw/MoOOIOayY0p+vZq6zOxzblZRV0OuKWexe0osr7p7HmEs62R1S0Dmpi7EiEg98AYwDXgeGGWPyT9Q8IyJpxpgdIpICfA/cYYyZeaJytOlGBbs1W/bS7gwniW3WsXtpj3o7Ds7mnfl0GbyR/DVd6TRyBku/Glhvz8XpqrOxbowxecB04GIgHVjmSfJNgZ9FJLWKfXZ4lruxviTqSYcmpap3RvOGXHzzMvauyOSxN+tnpWblxlza9dpB/rr23PTYbJb/d5AmeS+pTa+bZE9NHhGJBIYCS4wxKcaYlsaYlsA2oLsxZucx+0aLSIPK58Aw4Je6/QhKBab3/tmX0JSN/OPBhhSVlNsdjk8tWLmDbn0KKd7ZgkdeWcZrDw6wO6SgVpsafWPgJxFZDizCaqOfXN3GIpImIlM8LxsBs0VkGbAQ+MYYM+10g1YqGMREhvGnx/ZQtrsV//dA/eluuX77fgYOLaY8P5EXP1zHQzf2tDukoKc3TCllI7fbkNI1i33ZGaxZ6w767pb7DhTTskc2BRvPYML7q7nrmq52hxQ0dDx6pfyUwyG8+UI8piwm6LtblpW76HTuMgrWdeSuJ7I0yfuQJnqlbHbxwAw6nW91t/x6dvB2t+x3zSx2LOzDpXfNYsIf+tkdTr2iiV4pPzDpxY5IWCHX35qH2+1/zamna+Iny8j6fDAdL5jB5xPqyZCRfkQTvVJ+IJi7W+YVlvCHO2IJSdzKj+9V2YSsvEwTvVJ+4j+P9yMsZUPQdbccect8ynen8+jTu0lJiLY7nHpJE71SfiIqIpQHHttL2e5W/GZ8cHS3/PSntcz5sD+tBs/mgdE97A6n3tJEr5QfeejGTBp2WsyXr3Zl9aZaD/jql8rKXVw/pgSJzGfau+3tDqde00SvlB9xOIR/v5wAZVFcPnaV3eGclvPGzKJgXWduHP9r0N8f4O800SvlZ0b2b023i+ayetoAPv5hjd3hnJL12/cz/YMeNOkzT4c38AOa6JXyQ1+83BWJ3sfNtxcHZHfLm/6yDMoa8Ow/GtodikITvVJ+qUVqHNeNW0X+mq7c++x8u8M5KQtW7uCnD7qTmrmAK85ua3c4Ck30Svmttx7uT0TTNTz/t2bsyS+yO5xacbsN516yB4zw4WuN7Q5HeWiiV8pPhYU6efLpElz7m3LV3QvtDqdW3pu6moJ1nbnyzp8Z3K253eEoD030SvmxO67qQrO+c/np/V7M+2W73eHU6NF/7YOQYp76Q1e7Q1FH0ESvlJ/78JXmYIRrbtlsdygn5HYbNi5sR8bAxbRIjbM7HHUETfRK+bn+nZsy6DcL2DKnHy9MWmZ3ONV6b+pqTFFDBmhvSr+jiV6pAPDpc71wxu/g/j+EUVbusjucKj3xwh4IPcjDYzvbHYo6hiZ6pQJAUlwUt/5pE8Vb23HTo/43Dk5ZuYt1Wc1J6bBSm238kCZ6pQLEs3/oS2ybZbw34Uw278y3O5yjjH5oDuW5Lbnqmgq7Q1FV0ESvVIBwOISXJoZhDjbk8tuW2B3OUb54rxHR6b/w3H197Q5FVUETvVIB5DfnteOMc2eT9WV/pszbYHc4ACz+NYeS7Wdw9gV7cDjE7nBUFTTRKxVgPnu1HYQWMXrsXrtDAeCT76xun5cMS7I5ElUdTfRKBZgO6clcNGYJuct68vBri+wOh5lzS8BZxuVnZ9gdiqqGJnqlAtB/nqicdjCJwuIyW2NZszyO6GbZxMdE2BqHqp4meqUCUExkGH/++17Kc9O55j77uluWlbvIW59Bq46BPRtWsNNEr1SAeujGniR3XcQ3b3Tnlw25tsQwdd5GKGtAn95OW8pXtVNjoheRCBFZKCLLRGSliDxyzPp7RcSISJVXYkRkuIisEZF1IjK+rgJXSsF7ryRBeSSXjfnVlvK/+l8OAJcOTbOlfFU7tanRlwJDjDFdgK7AcBHpAyAizYBzgS1V7SgiTuBFYATQHrhWRHSWYKXqyHm90+lx6Ryyf+zPu1N9P8fs/AUGIvI5t2dLn5etaq/GRG8shZ6XoZ5H5dxmE4D7j3h9rF7AOmPMBmNMGfARcPHphayUOtLnL3ZDYvZy+50VPp92cNPKZBJbryPEqa3A/qxW/zoi4hSRpcBu4HtjzAIRuQjYbow50XB6TYCtR7ze5nmvqjLGiMhiEVmcm2tPe6NSgah5ozh+e/evFKzrzB1P+u7C7J78Ioq3t6Fd1wKflalOTa0SvTHGZYzpCjQFeolIZ+DPwEM17FrVbXJVVjmMMa8ZYzKNMZnJycm1CUsp5fHaX/oR2Xw1rzzeit37D/qkzEk/rAN3CIP6R/qkPHXqTur3ljEmD5iO1fySDiwTkU1YXwA/i0jqMbtsA5od8bopsOMUY1VKVSMs1MnTz5Tjzm/M5Xf65iaqb2fuA+DqYek+KU+dutr0ukkWkXjP80hgKLDEGJNijGlpjGmJldC7G2N2HrP7IqCNiKSLSBhwDfB1XX4ApZRl7OWdaTFgDrM/6sPs5du8Xt6aNQ4kOpfOrVO8XpY6PbWp0TcGfhKR5ViJ+3tjzOTqNhaRNBGZAmCMqQBuB74FVgOfGGNWnn7YSqmqfPxKOoiba8ZsrXnj07R7ezThCfu8Xo46fbXpdbPcGNPNGNPZGNPRGPNoFdu0NMbs8TzfYYw5/4h1U4wxbY0xrY0xf6/b8JVSR+rdIY0hoxayfUFfnvnAe0MZFxaXsW/NmbTtfuyPeOWPtE+UUkFm0oTeOBO28ef7oikp885EIO9O+RXKoxk+NNwrx1d1SxO9UkEmMTaSOx/cSsmOtozx0rSDU36ymmxGX6gjVgYCMca3N1jURmZmplm8eLHdYSgVsNxuQ/wZKzi4M5Xtm6NITYypcruNO/J44ePVZC0rpbQUykrB4YTERLhtVFMuGlB1Is8YMpuNWRm48o/taKfsIiJZxpjMqtaF+DoYpZT3ORzCv54Sbr40hWvumc70fw8+tC576z7GPrKcBT81pHBjezCe6f+cZeAsBeOA8mi+ew0aZCzngT+X88DoHkcdf/fWWGLTcgBN9IFAm26UClJjLulE0z7zmPFBT5Zm76KopJwRY6fTNiOUH98cCMDAUbN46dPlHCwux1SEYUobYMqi+WVDLiNvn07hzsb8aWwGe/KLjjp2RVkoIWE6EXig0ESvVBB754U0cIfSe2Ahia02Me2VwaR2XsWXMzZQsL4TM94ZzNjLOxMVEXrUfh3Sk/nvxMFMeH07lMRx01+PvgkrIqaY0iK9EBsoNNErFcSG9GhB15HzKNvZmoriaB58aSE5i3pz8cDaXUS946ouJHVexJcv9OXxd7IOve9wunG7NH0ECv2XUirIfft2JqMfmsW29bE8NrbXSe3rcAiLv22LI2Yv//zH4Ut6zhA37gq9xBcoNNErFeRSEqJ5+5Gzqu15U5MWqXE067SBoj2JgNWjJz8nkaj4whr2VP5Cv5KVUjXq1LWczbOb8Z9vV7NjdzGlO7tz2eiZdoelakn70SulavTLhly6dKvAXRQPxklI/E62ZzckJSHa7tCUx4n60WvTjVKqRh1bJfPTjArOPGcR3S6cx4K5YZrkA4g23SilamVg12asntas5g2V39EavVJKBTlN9EopFeQ00SulVJDTRK+UUkFOE71SSgU5TfRKKRXkNNErpVSQ00SvlFJBzi+HQBCRXGCzD4pKAvb4oJxAoefjaHo+jqbn43j+dE5aGGOSq1rhl4neV0RkcXVjQ9RHej6OpufjaHo+jhco50SbbpRSKshpoldKqSBX3xP9a3YH4Gf0fBxNz8fR9HwcLyDOSb1uo1dKqfqgvtfolVIq6GmiV0qpIFfvEr2IdBWR+SKyVEQWi0ivY9Y3F5FCEbnXrhh9rbpzIiLnikiWiKzwLIfYHasvnOhvREQeEJF1IrJGRM6zM05fEZGPPediqYhsEpGlnvdDReQdz9/HahF5wOZQfaK68+FZ11lE5onISs95ibAx1MOMMfXqAXwHjPA8Px+Yfsz6z4BJwL12x2r3OQG6AWme5x2B7XbHavP5aA8sA8KBdGA94LQ7Xh+fm6eBhzzPrwM+8jyPAjYBLe2O0cbzEQIsB7p4Xjf0l7+P+jiVoAFiPc/jgB2VK0TkEmADcND3YdmqynNijFlyxDYrgQgRCTfGlPo4Pl+r7m/kYqzEVgpsFJF1QC9gnu9D9D0REeAqoPKXnQGiRSQEiATKgAM2hedzVZyPYcByY8wyAGPMXrtiO1Z9TPR3Ad+KyL+wmq76AYhINPBH4Fyg3jTbeNxFFefkGJcDS+pBkofqz0cTYP4R223zvFdfnAXsMsZke15/ivXll4NVo7/bGLPPruBscOz5aAsYEfkWSMaqFDxpW3RHCMpELyI/AKlVrPozcA7WH+RnInIV8CYwFHgEmGCMKbS+qIPLKZ6Tyn07AE9g1ViCwimej6r+MIKif/KJzocx5ivP82uBD49Y1wtwAWlAAjBLRH4wxmzwarA+cIrnIwQYAPQEioAfRSTLGPOjV4OthXrXj15E8oF4Y4zx/PTKN8bEisgsoHKK+3jAjdX29oJNofpMdefEs64p8D/g98aYOXbG6Ssn+Bt5AMAY80/Pdt8CDxtjgr7pxtM8sx3oYYzZ5nnvRWC+MeY9z+u3gGnGmE/si9Q3qjkf1wDDjTGjPa//ApQYY56yLVCPetfrBqu9dZDn+RAgG8AYc5YxpqUxpiXwLPCP+pDkPao8JyISD3wDPFBfkrxHlecD+Bq4RkTCRSQdaAMstCE+OwwFfq1Mah5bgCFiiQb6AL/aEp3vVXU+vgU6i0iU54tgELDKluiOEZRNNzW4CXjO8w9RAoyxOR5/UN05uR3IAP7iqZ0ADDPG7LYhRl+q8nwYY1aKyCdY/3krgNuMMS77wvSpazi6mQLgReBt4BesZq23jTHLfR2YTY47H8aY/SLyDLAIq0lvijHmGzuCO1a9a7pRSqn6pj423SilVL2iiV4ppYKcJnqllApymuiVUirIaaJXSqkgp4leKaWCnCZ6pZQKcv8PTvTtkKXzblwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  10439366 5443037 0.5068420065378582\n",
      "compare to Census 10439388.0 Leip has Trump + Biden = 5443067.0 0.5068420065378582\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 10439388.\n",
    "atlasTrump = 2758775.\n",
    "atlasBiden = 2684292.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 10439366 10439366.0\n",
      "state Trumpers before, after slice opn are 2758759 2758759.0\n",
      "state Bideners before, after slice opn are 2684278 2684278.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9c8e462-1ac7-46a9-bd63-feda864d41f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = float(nTracts)**0.5 ... #trying to figure out optimal G for number of tracts and districts.  come back to this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 105 grids for 14 districts\n",
      "starting grid no 0 at x = -82.63043966666667, y = 33.95539649999999 \n",
      "starting grid no 5 at x = -82.63043966666667, y = 35.98058149999999 \n",
      "starting grid no 10 at x = -82.197551, y = 35.17050749999999 \n",
      "starting grid no 15 at x = -81.76466233333336, y = 34.360433500000006 \n",
      "starting grid no 20 at x = -81.76466233333336, y = 36.38561849999999 \n",
      "starting grid no 25 at x = -81.33177366666666, y = 35.5755445 \n",
      "starting grid no 30 at x = -80.898885, y = 34.76547049999999 \n",
      "starting grid no 35 at x = -80.46599633333335, y = 33.9553965 \n",
      "starting grid no 40 at x = -80.46599633333335, y = 35.9805815 \n",
      "starting grid no 45 at x = -80.03310766666665, y = 35.1705075 \n",
      "starting grid no 50 at x = -79.60021900000001, y = 34.360433500000006 \n",
      "starting grid no 55 at x = -79.60021900000001, y = 36.38561849999999 \n",
      "starting grid no 60 at x = -79.16733033333333, y = 35.5755445 \n",
      "starting grid no 65 at x = -78.73444166666668, y = 34.76547049999999 \n",
      "starting grid no 70 at x = -78.30155300000001, y = 33.95539649999999 \n",
      "starting grid no 75 at x = -78.30155300000001, y = 35.98058149999999 \n",
      "starting grid no 80 at x = -77.86866433333334, y = 35.1705075 \n",
      "starting grid no 85 at x = -77.43577566666667, y = 34.360433500000006 \n",
      "starting grid no 90 at x = -77.43577566666667, y = 36.38561849999999 \n",
      "starting grid no 95 at x = -77.002887, y = 35.5755445 \n",
      "starting grid no 100 at x = -76.56999833333333, y = 34.76547049999999 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 773236.7474833293 6169820.7161057 77.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 14   #NC congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if tractGeom[t].type == clipPoly.type :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if vtdGeom[p].type == clipPoly.type :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3f5a471b-867e-4db1-abdc-55f4b3d1f27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(p,vtdGeom[p].area)  #this will spit out the precinct number where we choked (again)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b538ed92-bf09-41ce-9758-8a764cbff950",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigh, this precinct's geom is still whacked.  Convex-hull it\n",
    "# vtdGeom[2034] = vtdGeom[2034].convex_hull  #after this line, go back up and redo the grid code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5134096134424555e-06 1.7770532499995762e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5 4.0 1 40.8 1.3786 170418.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 11 3 517135.91197427805 1.4545\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 13 4.0 3 36.8 1.154 439698.3\n",
      "I am working on tract number 20 of 2672 tracts\n",
      "I am working on tract number 40 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 48\n",
      "I am working on tract number 60 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 77\n",
      "I am working on tract number 80 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 82 5.0 1 46.7 1.0915 275254.6\n",
      "we have 2 non-opposing shorted wedges for tract no 88\n",
      "I am working on tract number 100 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 109\n",
      "we have 2 non-opposing shorted wedges for tract no 112\n",
      "we have 2 non-opposing shorted wedges for tract no 116\n",
      "I am working on tract number 120 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 122\n",
      "7 yoyos for tract,wedge,wedgePop,r= 124 0 516022.03883094277 2.0409\n",
      "8 yoyos for tract,wedge,wedgePop,r= 124 0 24197.823987181124 1.0205\n",
      "9 yoyos for tract,wedge,wedgePop,r= 124 0 533452.127353005 2.1609\n",
      "9 yoyos for tract,wedge,wedgePop,r= 124 0 412213.58543790184 1.5907\n",
      "10 yoyos for tract,wedge,wedgePop,r= 124 0 106628.37422373734 1.3056\n",
      "11 yoyos for tract,wedge,wedgePop,r= 124 0 231006.52586621098 1.4481\n",
      "12 yoyos for tract,wedge,wedgePop,r= 124 0 158005.60441139224 1.3768\n",
      "I am working on tract number 140 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 159\n",
      "I am working on tract number 160 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 165\n",
      "I am working on tract number 180 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 192\n",
      "I am working on tract number 200 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 209\n",
      "I am working on tract number 220 of 2672 tracts\n",
      "I am working on tract number 240 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 247\n",
      "we have 2 non-opposing shorted wedges for tract no 253\n",
      "I am working on tract number 260 of 2672 tracts\n",
      "I am working on tract number 280 of 2672 tracts\n",
      "I am working on tract number 300 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 313\n",
      "we have 2 non-opposing shorted wedges for tract no 314\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 318 2.0 1 90.0 0.5012 43974.3\n",
      "I am working on tract number 320 of 2672 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 331 1 165970.83200737936 0.4747\n",
      "8 yoyos for tract,wedge,wedgePop,r= 331 1 549499.9556809126 1.6249\n",
      "8 yoyos for tract,wedge,wedgePop,r= 331 1 343180.3212414504 1.0498\n",
      "9 yoyos for tract,wedge,wedgePop,r= 331 1 190950.89020126083 0.7622\n",
      "10 yoyos for tract,wedge,wedgePop,r= 331 1 304882.7024541897 0.906\n",
      "11 yoyos for tract,wedge,wedgePop,r= 331 1 223795.81980513164 0.8341\n",
      "12 yoyos for tract,wedge,wedgePop,r= 331 1 258166.31649891022 0.8701\n",
      "13 yoyos for tract,wedge,wedgePop,r= 331 1 237063.35861987915 0.8521\n",
      "I am working on tract number 340 of 2672 tracts\n",
      "I am working on tract number 360 of 2672 tracts\n",
      "I am working on tract number 380 of 2672 tracts\n",
      "I am working on tract number 400 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 402\n",
      "we have 2 non-opposing shorted wedges for tract no 407\n",
      "I am working on tract number 420 of 2672 tracts\n",
      "I am working on tract number 440 of 2672 tracts\n",
      "I am working on tract number 460 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 460 2.0 2 90.0 0.7614 273823.2\n",
      "I am working on tract number 480 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 492\n",
      "I am working on tract number 500 of 2672 tracts\n",
      "I am working on tract number 520 of 2672 tracts\n",
      "I am working on tract number 540 of 2672 tracts\n",
      "I am working on tract number 560 of 2672 tracts\n",
      "I am working on tract number 580 of 2672 tracts\n",
      "I am working on tract number 600 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 619 14.0 0 91.0 2.0315 193782.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 619 15.0 0 91.0 1.973 193782.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 619 20.0 0 91.0 1.9799 193782.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 619 25.0 0 91.0 1.9766 193782.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 619 0 193782.77719503728 2.095\n",
      "loop31.0, tr619,wedgePops740588.73, 161286.7, 192667.7, 192985.2, 193649.2, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 194794.1,-1.0475, 194794.1,0.0052, 194794.1,0.0033, 194794.1,0.006\n",
      "loop32.0, tr619,wedgePops744615.61, 161286.7, 194306.1, 194420.8, 194602.0, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 194794.1,-1.0475, 194794.1,0.0013, 194794.1,0.0007, 194794.1,0.0007\n",
      "I am working on tract number 620 of 2672 tracts\n",
      "I am working on tract number 640 of 2672 tracts\n",
      "I am working on tract number 660 of 2672 tracts\n",
      "I am working on tract number 680 of 2672 tracts\n",
      "I am working on tract number 700 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 719\n",
      "I am working on tract number 720 of 2672 tracts\n",
      "I am working on tract number 740 of 2672 tracts\n",
      "I am working on tract number 760 of 2672 tracts\n",
      "I am working on tract number 780 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 799\n",
      "I am working on tract number 800 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 808\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 810 2.0 0 90.0 0.7369 258189.5\n",
      "we have 2 non-opposing shorted wedges for tract no 814\n",
      "we have 2 non-opposing shorted wedges for tract no 818\n",
      "I am working on tract number 820 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 830 11.0 1 47.0 1.9156 230540.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 830 1 230540.56014155224 1.9516\n",
      "I am working on tract number 840 of 2672 tracts\n",
      "I am working on tract number 860 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 866 3.0 1 42.4 0.89 193244.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 875 3.0 1 86.9 0.5955 222947.2\n",
      "I am working on tract number 880 of 2672 tracts\n",
      "I am working on tract number 900 of 2672 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 910 1 338784.11363111134 1.2395\n",
      "8 yoyos for tract,wedge,wedgePop,r= 910 1 18974.941545735695 0.6198\n",
      "9 yoyos for tract,wedge,wedgePop,r= 910 1 338784.11363111134 1.2277\n",
      "10 yoyos for tract,wedge,wedgePop,r= 910 1 104493.56490895679 0.9237\n",
      "11 yoyos for tract,wedge,wedgePop,r= 910 1 325327.76835878636 1.0757\n",
      "12 yoyos for tract,wedge,wedgePop,r= 910 1 206807.38759436848 0.9997\n",
      "13 yoyos for tract,wedge,wedgePop,r= 910 1 279923.756565105 1.0377\n",
      "13 yoyos for tract,wedge,wedgePop,r= 910 1 243181.3258382219 1.0187\n",
      "I am working on tract number 920 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 931\n",
      "we have 2 non-opposing shorted wedges for tract no 939\n",
      "I am working on tract number 940 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 3.0 0 83.3 0.8634 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 4.0 0 83.3 0.7925 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 5.0 0 83.3 0.7274 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 6.0 0 83.3 0.6677 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 7.0 0 83.3 0.6128 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 8.0 0 83.3 0.5625 267018.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 9.0 0 83.3 0.5163 267018.1\n",
      "I am working on tract number 960 of 2672 tracts\n",
      "I am working on tract number 980 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 981 3.0 3 40.3 0.8445 284751.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 981 4.0 3 40.3 0.731 284751.5\n",
      "I am working on tract number 1000 of 2672 tracts\n",
      "I am working on tract number 1020 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1028\n",
      "I am working on tract number 1040 of 2672 tracts\n",
      "I am working on tract number 1060 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1068\n",
      "I am working on tract number 1080 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1092\n",
      "I am working on tract number 1100 of 2672 tracts\n",
      "I am working on tract number 1120 of 2672 tracts\n",
      "I am working on tract number 1140 of 2672 tracts\n",
      "I am working on tract number 1160 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1170\n",
      "I am working on tract number 1180 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1189 5.0 0 86.6 0.5839 188395.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1189 6.0 0 86.6 0.5793 188395.5\n",
      "I am working on tract number 1200 of 2672 tracts\n",
      "I am working on tract number 1220 of 2672 tracts\n",
      "I am working on tract number 1240 of 2672 tracts\n",
      "I am working on tract number 1260 of 2672 tracts\n",
      "I am working on tract number 1280 of 2672 tracts\n",
      "I am working on tract number 1300 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1302\n",
      "we have 2 non-opposing shorted wedges for tract no 1304\n",
      "I am working on tract number 1320 of 2672 tracts\n",
      "I am working on tract number 1340 of 2672 tracts\n",
      "I am working on tract number 1360 of 2672 tracts\n",
      "I am working on tract number 1380 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1383\n",
      "we have 2 non-opposing shorted wedges for tract no 1386\n",
      "we have 2 non-opposing shorted wedges for tract no 1388\n",
      "I am working on tract number 1400 of 2672 tracts\n",
      "I am working on tract number 1420 of 2672 tracts\n",
      "I am working on tract number 1440 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1443 6.0 0 86.9 0.3404 206526.2\n",
      "we have 2 non-opposing shorted wedges for tract no 1446\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1447 3.0 0 107.9 0.5337 143773.7\n",
      "we have 2 non-opposing shorted wedges for tract no 1450\n",
      "we have 2 non-opposing shorted wedges for tract no 1451\n",
      "we have 2 non-opposing shorted wedges for tract no 1452\n",
      "we have 2 non-opposing shorted wedges for tract no 1454\n",
      "we have 2 non-opposing shorted wedges for tract no 1456\n",
      "I am working on tract number 1460 of 2672 tracts\n",
      "I am working on tract number 1480 of 2672 tracts\n",
      "I am working on tract number 1500 of 2672 tracts\n",
      "I am working on tract number 1520 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1526 3.0 2 90.0 0.9098 197285.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1526 4.0 2 90.0 0.8717 197285.0\n",
      "I am working on tract number 1540 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1558 8.0 1 36.3 1.7675 593989.0\n",
      "I am working on tract number 1560 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1560\n",
      "I am working on tract number 1580 of 2672 tracts\n",
      "I am working on tract number 1600 of 2672 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1608 3 319939.97114102234 1.6843\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1610 4.0 3 36.3 1.3296 427451.4\n",
      "I am working on tract number 1620 of 2672 tracts\n",
      "I am working on tract number 1640 of 2672 tracts\n",
      "I am working on tract number 1660 of 2672 tracts\n",
      "I am working on tract number 1680 of 2672 tracts\n",
      "I am working on tract number 1700 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1701\n",
      "I am working on tract number 1720 of 2672 tracts\n",
      "I am working on tract number 1740 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1756 2.0 3 90.0 0.8399 186928.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1756 3.0 3 90.0 0.8382 186928.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1756 4.0 3 90.0 0.8365 186928.9\n",
      "I am working on tract number 1760 of 2672 tracts\n",
      "I am working on tract number 1780 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1794\n",
      "I am working on tract number 1800 of 2672 tracts\n",
      "I am working on tract number 1820 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1822\n",
      "we have 2 non-opposing shorted wedges for tract no 1831\n",
      "I am working on tract number 1840 of 2672 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1841 0 212746.15925457064 1.6644\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1841 0 97475.75086527954 0.8322\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1841 0 212746.15925457032 1.6629\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1841 0 127517.0333013223 1.2475\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1841 0 199643.23995287003 1.4552\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1841 0 144667.97966690903 1.3514\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1841 0 168865.79020383762 1.4033\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1843 0 229136.74332220788 1.8164\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1843 0 84414.41158468567 0.9082\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1843 0 245419.2230011989 1.908\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1843 0 94015.69243197714 1.4081\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1843 0 142558.4323640183 1.658\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1843 0 213297.2943055751 1.783\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1843 0 168509.3506207248 1.7205\n",
      "I am working on tract number 1860 of 2672 tracts\n",
      "I am working on tract number 1880 of 2672 tracts\n",
      "I am working on tract number 1900 of 2672 tracts\n",
      "I am working on tract number 1920 of 2672 tracts\n",
      "I am working on tract number 1940 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1944 4.0 3 38.9 1.0706 409597.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1948 3.0 3 36.4 0.8928 258008.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1950 5.0 2 90.0 1.4971 187409.1\n",
      "I am working on tract number 1960 of 2672 tracts\n",
      "I am working on tract number 1980 of 2672 tracts\n",
      "I am working on tract number 2000 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2003 3.0 1 90.0 0.9389 193777.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2003 7.0 1 90.0 0.7997 191836.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2003 8.0 1 90.0 0.7826 191836.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2003 9.0 1 90.0 0.7659 191836.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2016 1 232906.70186050705 1.6871\n",
      "I am working on tract number 2020 of 2672 tracts\n",
      "I am working on tract number 2040 of 2672 tracts\n",
      "I am working on tract number 2060 of 2672 tracts\n",
      "I am working on tract number 2080 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2085\n",
      "I am working on tract number 2100 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2100\n",
      "we have 2 non-opposing shorted wedges for tract no 2101\n",
      "we have 2 non-opposing shorted wedges for tract no 2109\n",
      "I am working on tract number 2120 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2134 10.0 0 89.4 1.1041 188704.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2134 11.0 0 89.4 1.0941 188704.1\n",
      "I am working on tract number 2140 of 2672 tracts\n",
      "I am working on tract number 2160 of 2672 tracts\n",
      "I am working on tract number 2180 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2193 3.0 1 37.9 0.8238 459967.1\n",
      "I am working on tract number 2200 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2214\n",
      "we have 2 non-opposing shorted wedges for tract no 2216\n",
      "I am working on tract number 2220 of 2672 tracts\n",
      "I am working on tract number 2240 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2253\n",
      "I am working on tract number 2260 of 2672 tracts\n",
      "I am working on tract number 2280 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2292 3.0 3 45.0 0.7216 380635.8\n",
      "I am working on tract number 2300 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2303 4.0 0 83.4 0.7762 282267.4\n",
      "I am working on tract number 2320 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2328\n",
      "I am working on tract number 2340 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2343 7.0 1 45.1 2.1091 256464.6\n",
      "I am working on tract number 2360 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2379\n",
      "I am working on tract number 2380 of 2672 tracts\n",
      "I am working on tract number 2400 of 2672 tracts\n",
      "I am working on tract number 2420 of 2672 tracts\n",
      "I am working on tract number 2440 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2456\n",
      "I am working on tract number 2460 of 2672 tracts\n",
      "I am working on tract number 2480 of 2672 tracts\n",
      "I am working on tract number 2500 of 2672 tracts\n",
      "I am working on tract number 2520 of 2672 tracts\n",
      "I am working on tract number 2540 of 2672 tracts\n",
      "I am working on tract number 2560 of 2672 tracts\n",
      "I am working on tract number 2580 of 2672 tracts\n",
      "I am working on tract number 2600 of 2672 tracts\n",
      "I am working on tract number 2620 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2628\n",
      "we have 2 non-opposing shorted wedges for tract no 2635\n",
      "we have 2 non-opposing shorted wedges for tract no 2637\n",
      "I am working on tract number 2640 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2658\n",
      "I am working on tract number 2660 of 2672 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000138413636039\n",
      "Average and max number of wedgePop loops per tract were:  6.36751497005988 32.0\n",
      "min,average,max of Home District areas were:  0.0 0.8141714156434419 2.231668525723435\n",
      "calculated statewide vote was 0.5097623702489185, should have been 0.5068420065378582\n",
      "calcd statewide Hispanic pop was 0.08740913691408812, should have been 0.08881510819181962\n",
      "calcd statewide Black pop was 0.1963604006838392, should have been 0.2009821094753111\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.3929640718562874\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000138413636039\n",
      "Average and max number of wedgePop loops per tract were:  6.36751497005988 32.0\n",
      "min,average,max of Home District areas were:  0.0 0.8141714156434419 2.231668525723435 for  NC\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start NC 9:42am, end 11:05am\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NC 19Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"19Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 0.7917933664171253 152 pop,GOP 0.6710526315789473\n",
      "98 0.6737144169653589 2723 pop,GOP 0.5795078957032684\n",
      "242 0.6893345067762032 1646 pop,GOP 0.5340218712029161\n",
      "312 0.704728206332563 2472 pop,GOP 0.5578478964401294\n",
      "353 0.7726937945904317 3587 pop,GOP 0.42709785335935324\n",
      "410 0.7566379144341472 4428 pop,GOP 0.6732158988256549\n",
      "428 0.6457711686587689 963 pop,GOP 0.6230529595015576\n",
      "429 0.747466719955907 4398 pop,GOP 0.6386994088221919\n",
      "430 0.7049554507671234 2641 pop,GOP 0.6558121923513821\n",
      "493 0.6403638246631124 706 pop,GOP 0.42492917847025496\n",
      "496 0.08678520910716722 547 pop,GOP 0.7367458866544789\n",
      "498 0.6587805567686278 647 pop,GOP 0.6476043276661515\n",
      "507 0.7694349950922447 2672 pop,GOP 0.29752994011976047\n",
      "537 0.7716270785193874 836 pop,GOP 0.465311004784689\n",
      "600 0.3042297984028251 786 pop,GOP 0.5508905852417303\n",
      "603 0.6232304315289104 7362 pop,GOP 0.6446617766911166\n",
      "661 0.7986825104667049 8962 pop,GOP 0.6420441865654988\n",
      "669 0.7799688779766693 8163 pop,GOP 0.6513536689942423\n",
      "719 0.652463692677748 4436 pop,GOP 0.5919747520288549\n",
      "816 0.7412283079301788 1076 pop,GOP 0.4247211895910781\n",
      "829 0.7265542955168172 3487 pop,GOP 0.4565529108115859\n",
      "1047 0.7863514001280668 7373 pop,GOP 0.5658483656584836\n",
      "1089 0.7679405543475434 1113 pop,GOP 0.44654088050314467\n",
      "1091 0.7546433850547674 1472 pop,GOP 0.5054347826086957\n",
      "1092 0.6360433917359234 809 pop,GOP 0.6699629171817059\n",
      "1093 0.6244089372038848 1046 pop,GOP 0.6606118546845124\n",
      "1184 0.7838094394392813 124 pop,GOP 0.7580645161290323\n",
      "1192 0.7667645502534126 4095 pop,GOP 0.5528693528693529\n",
      "1228 0.7919868085588989 9417 pop,GOP 0.5414675586704896\n",
      "1229 0.7664835891945196 3591 pop,GOP 0.657755499860763\n",
      "1230 0.6954093513948476 5491 pop,GOP 0.5902385722090694\n",
      "1255 0.7860612487340074 3717 pop,GOP 0.7188592951304815\n",
      "1256 0.7379440589071704 2929 pop,GOP 0.7388187094571527\n",
      "1257 0.7360137504724948 3585 pop,GOP 0.7207810320781032\n",
      "1258 0.6680614997480415 2489 pop,GOP 0.6536761751707513\n",
      "1284 1.0058822340656133e-14 6435 pop,GOP 0.6211344211344212\n",
      "1326 0.7799483464707824 790 pop,GOP 0.5417721518987342\n",
      "1346 0.7918602730026493 2150 pop,GOP 0.13767441860465116\n",
      "1357 0.40346850876853857 1442 pop,GOP 0.6414701803051318\n",
      "1393 0.7230793538024209 4778 pop,GOP 0.5443700293009628\n",
      "1395 0.7924645688843447 4683 pop,GOP 0.561178731582319\n",
      "1423 0.7187217367323112 3270 pop,GOP 0.6477064220183486\n",
      "1456 0.6960887702559772 1181 pop,GOP 0.5571549534292972\n",
      "1475 0.7558097356372268 2095 pop,GOP 0.7909307875894988\n",
      "1503 0.7091862522097596 1352 pop,GOP 0.6730769230769231\n",
      "1514 0.6884938340913777 1969 pop,GOP 0.6617572371762316\n",
      "1681 0.7977198054463162 10318 pop,GOP 0.6038961038961039\n",
      "1713 0.7678694336953483 2556 pop,GOP 0.72339593114241\n",
      "1716 0.7596194195760191 5759 pop,GOP 0.6740753603056087\n",
      "1877 0.5296307154823824 688 pop,GOP 0.5043604651162791\n",
      "1878 0.6506647393579104 2187 pop,GOP 0.7777777777777778\n",
      "1888 0.7563308654988137 802 pop,GOP 0.40399002493765584\n",
      "1957 0.7681682921802726 2699 pop,GOP 0.5005557613931085\n",
      "1983 0.7756179494646367 1894 pop,GOP 0.48310454065469904\n",
      "1997 0.7021597761688096 4594 pop,GOP 0.5742272529386155\n",
      "2034 0.7518043262590041 5379 pop,GOP 0.5263060048336122\n",
      "2035 0.7219466202664192 5702 pop,GOP 0.5007015082427219\n",
      "2036 0.7414544650641175 1914 pop,GOP 0.5721003134796239\n",
      "2044 0.6909383117546086 1655 pop,GOP 0.5438066465256798\n",
      "2046 0.7048322718303619 4582 pop,GOP 0.601484068092536\n",
      "2048 0.7155868916992463 3462 pop,GOP 0.5814558058925476\n",
      "2049 0.6996246133729211 4472 pop,GOP 0.603309481216458\n",
      "2341 0.7596485871362971 2030 pop,GOP 0.5600985221674877\n",
      "2352 0.6545886513457538 6755 pop,GOP 0.601480384900074\n",
      "2357 0.7784814808311893 1917 pop,GOP 0.45644235785080856\n",
      "2370 0.7356197824702307 1483 pop,GOP 0.30950775455158464\n",
      "2387 0.7687054664838943 2718 pop,GOP 0.3723325974981604\n",
      "2388 0.7940060923808837 864 pop,GOP 0.41435185185185186\n",
      "2418 0.736008985344312 1312 pop,GOP 0.5358231707317073\n",
      "2419 0.7481917373037706 1325 pop,GOP 0.2807547169811321\n",
      "2420 0.7672642719665647 1488 pop,GOP 0.3225806451612903\n",
      "2427 0.7776157440229466 2343 pop,GOP 0.29364063166880067\n",
      "2441 0.754245195859298 1318 pop,GOP 0.4347496206373293\n",
      "2442 0.786976632559583 1537 pop,GOP 0.13532856213402733\n",
      "2443 0.7886218798774348 2115 pop,GOP 0.5224586288416075\n",
      "2444 0.7991764457839959 865 pop,GOP 0.5028901734104047\n",
      "2446 0.7477972509308288 1649 pop,GOP 0.4111582777440873\n",
      "2447 0.7420426763566095 1360 pop,GOP 0.4360294117647059\n",
      "2448 0.7864415355367907 2096 pop,GOP 0.4818702290076336\n",
      "2449 0.7365597792372505 1640 pop,GOP 0.5469512195121952\n",
      "2452 0.7999036773111201 2333 pop,GOP 0.30218602657522503\n",
      "2453 0.780872871101313 1096 pop,GOP 0.593065693430657\n",
      "2454 0.7512279596461959 1395 pop,GOP 0.6724014336917563\n",
      "2456 0.7620054253678713 2042 pop,GOP 0.5558276199804114\n",
      "2457 0.7517969376972696 986 pop,GOP 0.3488843813387424\n",
      "2458 0.7583433718017116 1213 pop,GOP 0.6183017312448474\n",
      "2462 0.7547295526970096 1802 pop,GOP 0.4411764705882353\n",
      "2464 0.7110504888382292 1197 pop,GOP 0.5964912280701754\n",
      "2471 0.3015943387708416 1190 pop,GOP 0.7546218487394958\n",
      "2485 0.7302834030925686 7464 pop,GOP 0.5655144694533762\n",
      "2488 0.7208187790267924 6697 pop,GOP 0.6071375242645961\n",
      "2489 0.7000046722790557 6742 pop,GOP 0.63467813705132\n",
      "2511 0.3487816067314693 3185 pop,GOP 0.6740973312401883\n",
      "2512 0.0537535701362013 800 pop,GOP 0.81125\n",
      "2516 0.40668309049179024 7535 pop,GOP 0.6488387524883875\n",
      "2608 0.7246334122458534 4117 pop,GOP 0.579548214719456\n",
      "2650 0.7496621962096196 10592 pop,GOP 0.6211291540785498\n",
      "2654 0.28811045771672933 3122 pop,GOP 0.5762331838565022\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R5a0Vb9GlYWdeumAkfm8opbKsZtYbDINnTdkqt2kax1bgFW8zDN42Vbl05+dTUlxMe1XlmCR61D8ZBmt1ne5xGoKsMIyUdHsyjFW43WNM3nIeitIraftPBqraIyY7qOpZNKExgKmHxeitQeDDOaHDCXy+9nOklGl7UraE3gKArX9sZc7mOVxkP51i7+ZSzazjCeK6cLaKDAQIeL2s0/UKhX5zuMqlDATwezzMuukmABwZGVyqaUkR+58Mg/GmG9AdmhYV+xWGwR1uNz6vF6fLxfgybmJVQdeXmXLpfej6spQJfVVj4IrSFU27Oykx+tD+Bhw2CKso/dG0l60YvYnj2x3Pq0teZf2u9XTL7pYWGyyhtwBCLQIBrh1+A8+4vo+KYVnNrLuFs1Ui3nm3BLJVOoSrXAY8HoLBIAACCHg8bNH1pAj92pgb0Fpdp4uisMYweG3sWLweDzIYLPcmVhVUtW/YW454sqnJwKluDFxRulZb4Cs+Rn9L4E2c0OEEAL7f8n3ahN6K0R8hBIIBnv3hWRb9uiju8rdWvMWgtoP4y+mX8LSmcf3DD5cZtgE4VlG4TdM49+GHEwrbAByjKFymabQ//XS84UdYCQi7nfZJysfvHr4B2ex27C4X3VWVNYbBvW43y776ChkMRh+fi4qKWJSksQJF6YWm5fHww7kpDdvUVAzcMJaSl/cKhrE0Jfs/kujRogdNM5umN04vpax1P4MGDZIWyWXakmmSsUR/Hv7u4eiydcXrJGORT3z/RNKO92thoVw0bpz8tbDwsGU/FxbKB7Oy5FibTf7P4ZALJ05M2nGllHJ9YaH8Ytw4uT587HfHjZPn2mzyTJBukCeC7A6yE8juGRlyYRwbayuFhYtlVtYgabf3lVlZg2Rh4eIUHGOJzMoaIu32/jIra4gsLFyS9GMcaZz9+tmy94TeKT0GsECWoalW6KYe4w14+fuHf+e9Ve8B0DyrObsO7QLg/m/v5/+d/P/YdWgX92j3AHBJ70uSctzthsF0t5ug14vN5eJcTaONyePvoChco2ls0nU6qSodkpzaGNu0O0dVwWaDYBAHsJdQN3sAr8fDPF3HA+j6d6jqKShx7DGM+eh6Iap6POA0DUL2L9eWZOeUlxcDN4zlptm5VS88pusLYmr3LEhrW776wAntT2DG+hnsPrSbZlnNavz4ltDXU95c/ibPFT3HvK1/lqCdfvl0hrcbzkPfPcTY78ZS4i9h1BejeG/Ve/Ro0YMOTTok5di/6jrBcJw86PXyq66XEnoIiX2yBb4seigKN0+YwPO33ELA5yMTOAooIST4e/bswe0eYYp7zyol9oYxH7f7YrxeH3Z7BkI0we/3m4qF9QdgnmHwna5ziqoyXFHC8fTrTft9JbpuaOLSalS1ZzRmbhhL0fWFqOqgMoXVMDah67+jqmegKJ1M3y/H7b7VlPXzXJliX9ENQVUHx2TnxJ2DU21CN8+5qOqJKEr9zs6JxOnnbp7LecedV+PHt4S+HvLi/Be5+YubAXjs9McYNWQUDpuDDEcG8OckjqxHQs0Q7MLOtAumJe34bVUVm8sV9ejb1oJ6OGePHIkE3s/PZ/3q1TQhJPZ7gHn6d3jCA8Qejwdd/66U0Ot6YbR+e2gM2YeUEo9nP2PHPsbYsXcgCHCm2x0V9Zmahq6viMbTPR4YO7aAsWNdgAtVfQifrwSnMxNdvx84gNt9o0moXzpM7A1jE273i6YGIDdFxV7XF8Vk/SyKK+KJ3BAUpR+aNhldX4CqDk6JNx+6eV5ougl+VK/FXmmn0NDZkC/Xf2kJvUX1mf3zbG6dcStndjmTDy/98LBG3hDq/XpN/2vY9sc2WjVsxasXvIrDlrw/hTaKwrmaxq+6TltVPcybTwerDIMXR4/mYEkJ5kzmDMCZmRHNAgoGg/yx5yB5ea9ERU5Vj4/Wb7fbQUo/Pt8fBIMevv76K+bMmc01V19YqhnJd7qOqp6Ny+XC44FgsBlffbWCb7+9E0Xpj9f7OwBe734KCr6lQwd7TLhkIYrSD8PYgK6vRVW7o+sbwg1AIhOe1keFXlUHxmT9xK+gmegNQVH6VUrgQ97596jqCQkJtq7PjWneMrdeC32GI4PTOp/GjPUz0pJPbwl9HaS8P5Snjadp2bAl7/3fe3FFHiDLmcXU86em0kTaKEqtEPgIS3Udn9eLCNd2ilR4ChJKPcuwgS9ow46d/CfyCYi2ODKaommTUJQhaNr76Hoh2dlHcdttdwIBwE4wGKSkpIQd24tLNSMJhW/6o2mvMHZsAV99tQIpJT6fnzlzioBMk3W+8ExTc7hkEIaxAbf7meh3+fmXxDQA+TNNUlFy0LTnKozRJ3pDqAwh7/xvpqeEDysUbVU9sdT1UtUTq21Hbefsrmfz2drPWFu8luNaHFejx7aEvg5R4i/hyg+v5LMfP6Pw+kIGH106drr70G6+WPcFtw69lcYZjdNkZe2kcXY2wmbDLiUeKfFKiZ+wXJeU4HRk4vM6cLEXZAl2uR+vp1t0IFJRhqAoQ8jLewK/30/oFmEHQlkN33zxKeOfy2dncXE0Rg+hwdOxY118++2d+Hx+QreYfdjtDQgGJS6Xg9zcUwEHV199FbCf3NwzUZR+5OV9afLgAxQX70PTbkLX16OqXUvF6EPHyikzXPNn7D+xG0Jl0PXvTa0JQ58rEvrQzfOjIyZGD3B2t7MBmLF+hiX0FvHxBXyc99Z50drW6qsq++/ZX2qdD1Z/gC/o44qcK9JhYq3li0mTmDBqFCV+P16bjYFnnMGcWbNCOfzARddfT7PFPzLlpYkQ/k4icdoOkp2dSV7ek6jqSSjKMLKzWyFEJjabF6fwQlBik15kAJYsXkXbDoORlL7JKkovnn/+RkaNeoxg8BAZGZL8/BsoLi4JT6xy4HY/FvXUc3MvBEBVu+NyOfB4BEJkkJ3dDEXpdJjAl0coJh/b5rCsG8JKdH0pqtoPRemd8DFU9YSY1oQnJLRd5OZZFeYbBt/rOieoKkMq+eRoGEWmMNPQKh2/KnRq2okeLXowY/0MRg8fXWPHBSrOoyf0jFkELAVWAg+alt0K/Bj+/vEytj8rvM564L8VHU9aefSHEQwG5T8/+6dkLPLlhS/Lu2beJRmLfHTOo6XWO/XVU2X357rLYDCYJktrH6sLC+XfnE75V5DngRwOcrDNJu+98ko5csQI+W44h7+wsEhmuVrJhiAbgjzKZpfuk8+QGRnNpcPWQGY6GsirrrxeOp3HSCHaSJutrcy98gbZMitLNrHbZXZGpsxyDZF2uyqzss6QhYXLD7OlsHCRHDfuJVlYuKjU9+PGfSrt9qslXCXt9qvluHGfRpdNnDhP2u0PSXhQ2mwPy5NPflfeeONMWVi4RRYW/izHjftOFhb+XOb5jxs3VdrtwyUMlXa7IseNmxp3vcLCFTIr62xpt58us7LOloWFKyp1nQsLi+S4ceNlYWFRpbarCkWFhfKYrCzZym6Xx2RlyaJKzIMoLPxBZmUdLe32FjIr62hZWPhDCi09nDu+vENmPJwhD3gPJH3fVDOP3gOcJqXcL4RwAnOFEDOALOB8oK+U0iOEaBW7oRDCDkwAzgC2AvOFEJ9KKWtneb9ayvh545m4cCI3D76Z6wdez4x1M3jSeJKWDVtG1/ll3y/om3QeOOWBtJYY3mEY0UHY1imI0W8zDDbrOh0SLIS2QtcJBgLRAdgMwBsM8u077zBx9uzozF9FGYKmT+f1gjdYt2opP3w/hzlzvsMnBS48yCC8+cZbBGgECKSUvPnOd7w04SV2F29j4+YSJk6ebRrkXIKi9CllS6Q2jGGsJi/v3XD/1Z7hYmPm2PufxcYWL95FIBAaUQgGg8yevZnZs+288soihAgNZrpcdjTtGhTl8PTY7OyjECKIzUY09h8PXV8aM0i7tFJevdk7D3nMc8JPQcn3mL8Pj7cEAgHwevle1xP26kNhJvMg8Pc16tWf3fVsxs8bz7cbv+Uv3f9SY8etUOjDd4pIjMAZ/pHATcCjUkpPeL3f4mw+FFgvpdwAIIR4m9DNwRL6BPD4PQx7eRhLdyzltM6nkX9WPgDGVgObsHFp70uj6+bNyUMiuTzn8jRZGxL5L0wTpc7RtKSKfWz1y8sSKITWR1WxO534PB4k4IssCAZZHFPrRlGGYMfP2SdPwB8IIJDYsAOhcI4NHwEg9OfvJxDYy4LFq8nNvZCfCj7F4QitGwpf9I9rj2Gsxu2+15Qi+QiK0hNN+4+p2Ji5Hkrsv6gTaILPVxy2o0E4JXTTYUJvGEsZPfoxgkEfdruN/PzR5QzS9osZpK044yZepo1hFOF2/9WUNvlp0oX0BFXF6XJBuB7TCZVI3w2FmcyDwImFmZLFyR1PpoGzATPWz6hdQg9Rz3wh0BWYIKX8QQjRHThJCPEIobknd0kpYwtvHANsMX3eCgwr4xgjgZEAHTokZ+JOXefzdZ+zdMdSclrl8MUVX+C0OwHY8scWXHYXLrsLgOlrp/P8/Oe5behtdM/unjZ7402UihX6Xw2DrbpOO1WlbSVvAubqlwGvl80JFELroSgcf+21fD5xIiVS4gOcQpCZkcGAOALxva6HPEXAThC7EAhsSCRS+oADhPJ0DiKl5JVX8pkyZTKBgMRuP4obbvgnubnnHubNR9D15eEB1kga5XIUpSeK0i1G4EPk5vbh5ZdX4/f7+NPPsgONIfx0EQxKsrObxjnWAjweH8FgECklixevAv4W1y5F6Y2mPZFwjN6caWO327juuivIzb0UXZ8T4zHPSbrQD1EUPtK0KsXoFWVoeBC45mP0kL40y4SEXkoZAPoLIZoCHwkh+oS3bQYMB4YA7wohjg0/AUSIdxZxexdKKScBkyDUSjDhM6jHrPxtJQDfXP1NdLLTcsNg7/ItlARK2OfdxxPfP8H4eePp17ofj53xWDrNrXCi1K+GwUcmj/xCTauU2EerX4a371COJ7fRMFiv63RVVU7PzeWjadMIer04HQ7Ov/ZazsnNjVuwrWl2NgEpo9X+HPaQkNrsDgYqJ/DtnHlIGcmeISzAh5DSCfxBhw6uMkUeQFVzYsI05We9KEpbZs/+Px5/fDkff/wroZuMHXAReT6x2QTFxcHDts3Obk0wGE4nlfDyy9PDN6H+ZRyrdwICX4Suz2Xz5m3hUI+fQAAmTpzGtGlvk5//SIzHfFK5+6sqQxSl0oOwERRlaI0LvJmzu57N9LXTWbdrXY05ZpXKupFS7hFC6IQGWLcCH4aFvUgIEQRaADtNm2wF2ps+twN+qZbF9Rx/0M+ekj20aNCC3w6EomHb92+nRYMWLDcMbnO7WX9CCZwI2Y9nAzDsmGF8fNnHZDoyy9t1ymmtKJxjmigV681vjfHIt+p6pYQ+Uv2yohj9RsPgBVNN+ps1jQmaxiJdZ6Cq0ldRWG0YvJOXR19VpadpPxsXL6aTEOySkn1CEAgEop5X7149MOYvxOMpIRgEm82G0+lEyiwCAYnL5SI7uzl5eU+VGZ8OhWkeQdeXR2P0FaEobfnoo7ZceOF3fPzxtvC3Nuz2kB/lctlQ1baHbVdc7CP073cQcOD376egYHqVa+6EwjLn4/V6cTgc2GyCQCCcoyQlHo+X4uLdaNqnVY7RG8Y8U82h4VWys7ZzVtezAJj106zaI/RCiJaALyzyWcDpwGOE4vanAXo4jOMCfo/ZfD7QTQjRGdgGXAZYuX9lsHH3Rk6ddio/7/2Za/tfy9ritXRr3o3eLUNe1uLwIFTbORIRFGw5WTL46MEUXl+ITdSOitOtFaXMuHy7GI+8XRVKIxyjKBWGa9bH1KRfr+ucMWYMfcPbrTYMxpgakORpGj3DNetXTZ3KsVLSCdgqBMV2O38Eg7hcLq7MzeXy3Fx0fTbZ2c0pLi5GVU8B7Oj6XLKzmzN69H8rjE+HQjVlC7xhrDPVwfkznHP33b2ZMWM7Xm/InmefHUZx8UGys+3o+k+AD0VpF11fVXvjdDbC53MS+tf0M3XqTHJzz69S/rx5Nmvo5ucgkowKocHi7OzmVfaYDWMebveZpus3s16K/bHNjqVLsy7M+mkWtwy9pUaOmYhH3xaYFo7T24B3pZTThRAuYIoQYgWh2lBXSymlEOJo4GUp5TlSSr8Q4hZgJqHnzSlSypUpOpc6jZSS898+n5/3/kzX5l2ZuiQ0c/VB9cFoHG+AaRCqi+Fi2n3vMuR4tdaIfEW0VRQu1LQqx+gTpWtMU5SuMTeUZeEbZqSL1jJdp6eihDJ0/P7wwCu0kZLWNhuDrr+ev+bmMiyaoXO43YoylLy8p6oUnzaMhei6gaoqwFG43Y+aBmv/axJ7J9de2wvwkpvbEUVpwaRJy7nlli8IBCQZGXY07XLgALq+iuzsxthsNkJx/dbADvx+f5llDyrCPJv1T2+e8NWS2Gw2iot3V3q/EXT9u5jr9129FHoIlSF5bdlreAPe6FhbKkkk62YZMCDO917g73G+/wU4x/T5C+CL6plZ/znoO8jy35YDsGjkIpo82gSJjD7mAeQoCs9qGot1nQGqSk4tKjGQKG0VJWUCD3/G5i/Mz+dAcTFdVZXOMcfrG75hRjz6vuEbQZ/wDcJXUoKUEr+U4PfTu0OHqMiXh6qeVOn4tGEsxO2+NJrtcvXV/4oZrF2NonTDMPbidi8Oe/M2cnOdGMYORo36Dr8/FJ/3ePyMHv0pS5fOxe8PIIQtXIQNQCBEFi7XoSqXPQgNZH4SfnrJZvTo+/F4vASDQYQIVYKONyicKKp6Ssz1O6XK+6rtjOgyghcXvMi8rfM4uePJKT+eNTO2ltDQ1ZCcVjlIJI0zGjPvH/OQUjL0mNIeYY6iJE3g1xgGy3WdHFWlRx28acQSLzYfK/IAPRWFPE1jma6XitH3UBQe0jS+KSjg8ylTIBDA6XLRL8EQU0gIKxef1nWjVPkA2BMzWNszvN5uvN4godTxILq+G9hLMPjnv3AwCEVFawhl4xzCZvNitzsQQuJw2Lj22rPIzXUDQfLyppRbDrm8c4ycV05OL3S9kD179jJ+/LMEAkFGj/4vOTk9qxS6UZThaNrMeh+jBzi106nYhZ1ZP82yhP5IY2DbgXy4+kOAwwQ+2UTa60VE8RFNq7LY/2IYrCkoQAA9c3PTVswsXmw+ntBDSOx7xlnWQ1HooSickpvLUl2nn6rSu5Lpe5UROVVVSpUPyM0dQW7uxej6arKzs9H17UAmqtoMl8sW9ehVtRmQRUZGJh5PM8BHMLidkMg3AiR2+3aef/5Kiov3o6q9UJTuGMbSCsshJ36ukfo/TxMMSoLBoKkSZdX+fhVleL0W+AhNMpswvN1wZv00i/+d9r+UH88S+lrEJz9+wj7vPvaU7KFpZtOUHmt5WBSDgQB+r5flul4lof/FMPjg1FMJeDwArJkyhQvjNBqpCSqKzVeG3opSKYGvKooyCE17JxqjV5TIzNVM3O4CvN7IzNdcNG0ABQVbCRVUC6AordG0s9H17ezZU8zjj/9MKCciFDv3+5sDDRkz5vTo8XR9YdxyyNXhSKxEmQxGdBnBWH0sxQeLyW6QndJj1Y1RvCOA3w/+zp6SPQgETTKapPx4OaYm2g6XK9RurwpEUiYj+H0+tul6coyMw2+GwdK8PH6L09S7s6Jws6Zx9sMPlxm2qY0oyiDGjLnFJPKg65vwegPRypW6vgkIMG3aJiZP3oTbbTBp0np0fRuq2pqmTe2EKpRAJAtGSgc33/wJhvFzdL+qOgiXy4ndbi+3JELl7A/F7h9++F407ZO05qjXJUZ0GYFEom3UUn4sy6OvJRzwHgDg2bOfrZHZcj0UhUc0rUox+l8Mgy26TntV/TNlMuzRO5xOjolz0/jNMNih67RWVVpVUYB/MwxmmEosnK1ph+2rs6JUKPA/GQZrdZ3uqlqqt2xFy2oSVe2Ey2WPevSq2gldL8bjCRIMgscTZNSoIoLBP7DZ4NJLO2KzNSYQKCFUnkoCQQIBQUHBAhSlIxDpHvVShS0LK0u6JyHVRQYfPZimmU2Z9dOspPVrLgtL6GsJry17DSCl3vxywyiVsROJR0dYYxis1HV6lyP8vxgG75pmt16iaVz07belYvQCWJKXF5009ZthMMu0zYg4Ap0I8UosVHY/PxkG401jE3doWlTQy1tW0yhKezQtF13fhKp2QlHas3z5+uhM12AQgsESINTe8I03fiRUXd9JKHSzL7qvVat+idl35bpHVUSqi5jVVxw2B+7Obmb9NCvl5RAsoa8FHPQd5CnjKfq27svf+x6WsZoUIrNqIymFz2paqeydNYbBAyaRe7CMwdktMbNbt+g6w8aM4ejwuvEKm/0WCe+EBXpHFQQaqt+LdoNhMH3sWPweDzIYJOD1slbXo2K+NmYw17ysOiTaZs8wVpgagvRBUdqjKH9OLF+8eDNC/IyUmUAAIQKEqpMEMZVrC793EhL+AN9/vx7D2ICiHFvtcznc5tQXMavPjOgygg9Wf8CPxT/So0WPijeoIpbQ1wI+Wv0Re0r28NGlH6Xsrh6ZVRuZJLRY10sJ/cqYwdmVZQzOto+Z3do+tp5NHK/76PA2EYFunaBA/2oYbNN1jlFVSoB1uk6//HxEcTFtKxkC2hD21n1hkRc2G3aXi+4mWxpkZxMUIu6yqvJnE+xIlkv8JtiGsQK3+zbTes+WqpljGFuZOnV+uMdDgFBevCA0nUWGf0R4mQPCVTdDxdd86PralAh9TRQxK4slhsF8XWeIqtK/jozHxDKiywgAvlj3hSX09Y1nf3iW54qe4+jGR3Nxz4uZtGgSmY5MTumYugkikVm1EY8+tnJj7/DgbMSj712GyB2tKFyiadEY/dEx/2DxvO5WisIITatUjP5Xw+DjcLhHOBxslZIDgQAOl4tb44R+thoGP+s6HVWVdnH2H/HWCQax2Wz0OP10zh07NuqxrzcM3hw9Gn8wiM1u54r8/FLe/FpTWKt7OfaHarXMRlVPRlGGJ9xmL37T7j6m5ZvDE6PMefMRcY/UAPQiBOGbQeh7IQQZGTZUNTU1VaoySSwZLDEM/uF2R4/7sqbVSbHv1LQT/dv056M1H3GncmfKjmMJfQ2yY/8OPl7zMbd/eTsA63etZ/bPswG496R7Uxqjq2hWbQ9F4UFNqzBGDyGxjxX4CGUVNmulKJXywLeZQkQyGMQF7JeSgNfLupj8+K2GwRumMYArNe0wse8ek3p53tixHGsOXYWfeGQwiBSCfcXF0WVrDYOHTWGt+zQtrtiHarWcZQpjfJlwm72KmnaragdcLjslJUFC9WEj4h4Iv0pCVTQloZ5AoeXnn9+Tu+8+NanevGH8YIrJD6tWEbOqMl/X8ZqeUOfrep0UegB3ZzfPFT1Hib8kZYUJLaGvIeb8PIeTXw3NgGt/VHuKbigi05HJlR9eSfuj2vOg+mCV9rvYMCjSdYaqKgMq+EOvaFZt7OBsVYktbLbdFIJJNL/+GFOISDgceKXEFghgd7noFvO08XPMuMHPun6Y0B+rKNyhadGMmmNjb3ThJ56ImPcwHSNeWCue0Ov67JgwxmzGjLk7pv55/Bi9ovRB054tFaMvvbwdmnYFY8fO5quvNoYFPTQYGwrTeMjICOL1liDlPkIdQP20adMz6SLvdp9ruplNR1GGxRX4RMcmqsIQVcVlekIdkoQwW7o4qcNJPGU8xfxt8zmpY2qeiCyhryGe+eEZAN65+B0u6HFBtJDR51d8XuV9LjYMrjU9vk7VtArFPplsC6dZZmVn4ykupl2cUM52w+BTk7f9V01LSOzbKgoXaNphMfpucWrXdIwZN+ioqqwzDFbrOj1VlW7h9Y9VlMMEPkJXReHfmsYaXaeHqtLVtF6iYS1VPTkmjBG6sSfaBDs0AFt2LXtFacdFF7Xmm2++wu9vTEjMAXxccEEvzj67CzfdNAUpg4RKE7vYvn0neXlTw6mUfSu0oSLix+QP7yWU6NhEVemvKLysaXU+Rg9wYofQBLM5m+dYQl9X+Wj1R2zbt42P1nzE7cNuT2q+bFHM42uRrteY0G+LpFl6PNhDxdlxZGRwkaaVEvttMd72TwUF7NZ17NnZHCoupk05vWVjC6CVlR/fTlG4UtOiMfpDQJ4p1DJG06Jib2azYbBJ1+mkqnRQFLqGf2LprijcZwprlRWjV5ThPJv/JB9+8CF/u+hvSZ/KbxjLGT36fwSDPpxOO5dc8nd27rRx0UV9GDlyGHl5n2Lu6yPEQaZP/5TPPtsXFtsXUZS+GMYCdL0QVT0eRRlcKRvMMXmHw8HmzZswDOOwip6Jjk1Uh/6KUqcFPkJ2g2x6tezFnM1zUnYMS+hTyHsr3+OS90PC3qphK24fdntS9z805vF1aBIeX9caBqt0nV4VDDpG0iwJBkP5HuF0xa26HhX63wyDg5s3Y3M4CAIuu51fp05lm8+HDAY5ZLNBRgZnJ6G3bDtFiYZrPs3LKxVqWa3rpYR+nWEwr6CAVVOm4AqHg67RNDqUY0N3RSn3egD8YBjcO/o2vF4vC+fo5OT0TqjqZaLo+mK83lBrQICdO39i7NjrUJTQQKuq9iQjw2lqH7gbvz80MBspdwBe3O6LTZ72+5US+1BMfjoFBW8wZcoUJk+ezLRp09C0WaXEPtGxCYsQg9oO4ttN36Zs/5bQp5CibUUALL9pOcc2O5YGzgZJ3f8ARWGqpiUco6+ItYbBIyZP+N4yBh3BlGbp8SBM6YqRZiLmWaxOu52uN9xAJrBp8mQitXPtwSA+r5ftcXrLRvjJMPhR1zmuEjNVe8aEWnqaboDrDIPH3G58JSUgJa2BLK+XTbpertAnwtzwE1YkrDFX15Mq9Ko6AJcrIuSSr75axzffPMCdd55D06YZqGoOmvZfRo9+maKiZURy64Vw4nJJVHUQuq7HeNqFlfbqFWUYuv4tgYAvpna8YlpnSLljE5GWhKp6opV3D3Rp1oXXl72Ox++Jtg1NJpbQpwgpJXO3zKVz0870aVV23LWqLDMMFug6g1WVf44Zk5R9rooZdFxVxqAjhDo9RdIs48Xozfn0AmjSoQPHqCpbpk0LlUsIBgnYbNhcLtqU8STyk2HwpOnGc1eCM1W7KQpjNO2wGD2Esmv8Xi+R1sYeIWjkctGpgqehdaYnHfP+5hsGhbrO8arKieEnrEiM/sQkDxAqSg6a9ixjx77BV1+tQ0qB3+/j8cc/RoiDZGZmkp9/A0uWRHoBgdPp5PrrzyI396xwjN6Lw2EnGAzicNhR1eOrZEsitePLGpswtyQMDeha9XE6N+uMRPLz3p9T0l7QEvoUsM+zjzeWv8G8rfN4esTTSd//MsPgRtMs15c0Ldomrzr0ivGEe1UgVOW19Wurqtjs9pD3brfTVlVpoSicomnsTDBG/2PMTNUfKzFTtZuixI3L9zCdo93h4MRrr2VYbm653vw6w2Cc6YZzTzjmP98wuNj0e3hf0/hU05ir65yoqknx5g1jCbpehKoORVH6oyg5jB17Hd9880C4MXkIKUsoKdnDK698RiAQJJSJE2TAgK7k5v7F1PRbIKUDCIZfQym9CwwDQ9dRVJXBCditKAqaNstUO17BMAx0XUdV1bhduCKYWxJWt6xxfeHYZqHMqA27N1hCXxf479f/5bHvHwPg+PbHc/vw5MblARaYZrn6vV4W6HpShL67onCvpiUUo68IAbiEIAjYhCAyQ6CFotAiwf0eF5P7flwlPOSthhFtIm5OteymKPzHlF0T72YQS7wnnW6KQmH49xAIdQOhUNe5fcyYpIVrDGMJbvd1Js93SljsuzNhwvXcdNPEcLxeAvuQ8hCLFxvY7a2QUhAMBliwYDVu97/QtKdQlN7oukEgIJHSRiAg0XUDJ14uNd2w3tE0BisKRYYRvWkNjXNOiqJEBd0wDNzu0022fl2m2JvLGtvtgs2bN2IY846IOvRlYRb6VGAJfRJZV7wuKvIA/z3hvynp53pUdjZ+IbDZbDhcLgYnMUSQyKBjIuwI919FSqTfzw5dp2Ul9ysB5eqrEcDxubkJe/NbDYO3TCmdl8dMoCrL2y+Lsp50jlf/7OHrdLk4PsmhGl0vCnu+kdmyRShKfwBGjjwd8DBqVD5+/x/AISDUoPuGG05hw4Y/+Prr+QSDMrztEhSl92GNTlRVwdBnlbphGbpOEDjflLr7iabFFfs/bdVjvHS9TKGPlDUuKHiDqVOnMnnyK0yb9hqa9uURK/ZtGrUhw57Bxt0bU7J/S+iThMfvwV3gBmDlzSs5Lvs47DZ7BVtVnsWGwSOjR+MNT9Ufk5+fFG++MvxsGPyk63RRVTqWNUO2ivVtIqw3DJ4weZlKbm7C226OSencHGcCVWXopijcY3rSidwkhigK72taNEY/JMm/B1UdGvZ8I7NlS4c3Ro78Czk57Sgo+IypUz/A7w+Eu1T9BXAyZ85S07b9gUijk7dKNTpx4o3esAJ2F2s2/8pPBa8dNrBcntCrMeMTagW/b0UZiq7r+P3+UhPMjlShtwkbnZp2YtPeTSnZvyX0SWD62umMnzeeLX9sIf/MfHq17JWyY0WnfgeDCCHYVVzMUsNgerhM8Lm5uSkV/p8Ng0mmePVITYsr9i0VBbepvk1lvfloSYJwuGSNrsfNcY9Hh/BN5oDHg0cIRHb1u/eU9RQwRFGSLvARFKU/mjalVIz+8HVCJYdzc89F1+ejqkOi62naU+j6ElS1fzRGP98w+EHXaZudTaE+CxtehikK72gabxa8zgtT32Hi5AIy7IJGjpA8JDKwHIrZf51QjD5CdnY2oSCfCH9unuilqZc0y2rG3pK9qdm5lLLW/QwaNEjWFeZvmy8Zi2QsctKCSSk/3qLCQtk/K0v2tttl/6ws+e7EiXKwyyUHgBwIclhGhlxaWJiy42vjxsm77Xb5b5B32+1SGzcuJcdZV1goR2Zlyevsdvl3l0uOv/FGubwS51U4caK80emUI202eXNWllxfxWuysbBQfjVunNyYwmtaXQoL58lx456QhYXzyl2vqLBQtsvKki1sNtkYZBObTbbKypLzwuc2btwT0m5vIqGRtNubyFE3jpJPjRsnf0jBuRcWGjIj4ygJGRJcEuwyw5UhC2vxdU41I14bIYdNHlbl7YEFsgxNtTz6ShAIBthTsifa3zEog9w4/Ubswh4K17Q4LuU2DFAUpoSnfjfLzkb74AP8Pl90sDOZg7Px6BITr+6SohojkZIEWkEB706dyubJk5k+bRr5mkafBM5tT3FxaNJQnLrzibLJMHjR9PRyk6bRqZbMxDSMhei6QXZ2Y0aPvuuw2jPx+D78lOQPz2OINPOO5PvHVqK8MveqMvdVFeYZBnN0nZNUNVpKIYIAfD5fubH9+s5RGUexZe+WlOzbEvoyCAQDbN67maJtRSz/bTlZjixeW/YaG/dsJP/MfIYeM5SNezay8NeFTPnrlBoR+QgDFAUbcIPbjaekhKCURLqFJntwNpaOisJITaswRp8MuioKXxQUkOn1UiJltI5+IkIfW62yKrXl15vSO/1eL+t1vVYIvWEsxO2+FK/XhxAegkFPVLTLqj0DcEJ48Djg8RAIl2s2h2VCs14/K1WZMlnMMwzOMQ3uPp7/DA6HA58vnO9PEJsQtCojzBabAbTEVMyvPpRBAGjsaswfnj9Ssm9L6OOwff92Bk8azLZ92+Iuv/mLm6Pvm2U244IeF9SQZX+yQNfxeDz4pMRGqIbh4JNP5vZHH0354GxHRSlX4M0NQ9pWw5a1hsH8qVNpLCWNgD+EYO/mzawxDILAUl2nn6rSK84xuigKd5qqVValU1TXmKeXrrWkQqKuG9HZrTabDbvdjhCiwnrwQxSF/+Xn88kHH5DTvz9HNW16WL5/qBJl8gQ+wpyYrJzdxb8z8vqrmDxxMkIGsCMREv4zejR9cnJK2VRkGFxgukk8mZ/Pk6NHRz9PqaO16GORyJRk6YEl9IdxyHeIi9+9mG37tnFKx1O4fsD1nNX1LEr8JWQ5s9j2xzZeXfIqfVv3Zdu+bVzc62KaZTWrcTsHqypSCGyAK/zdcsOIu+4Kw2CJrtNfVRPyhqvDr4bBJ6bUxvM1rcpi/21BAQe93mivJJeUfDV5Ml9PmYJPCAJ+P06Xi8c0rUyx76IobDIMvsnL41hVrZRH3klRuEnTWK/rdK3ktomywDSzNpGJSkBMimQm+fl5FBfvrNALLzIM/hMWyMI5c/i0gpTJZHJSTFbOSarKycDb016lpCSAlKHxQq/Xy+yY0hGxpSW+/OCDw4r51QehP+Q7RJYzKyX7rlDohRCZwGwgI7z++1LKB4QQY4EbgJ3hVe+RUn4RZ/tNhDoVBwC/lLJyhTVqCCkl2/dv5/5v7+f7Ld/zzsXvxK002aJBC8afNT4NFpZGAsJmwxYINZ4QgAwEDovPrzAM7jClKY5PMMZdVWKrVW7TdUT4+8rUo19tGHw5dSq+cKmCDLs9NLAUDBIIBgkQ+p35vF6W6npcoYdQnD02S6iyYp+qcM0Cw+D/TL+b98ITlcojVHnSID//QYqLd0dTJBMh1queU0HKZDIZrih8oWnRGP3w8HE/1zTeKCjg9alT8fv9uFwuTo55cootLXHWRRexYs6c6HVrnp3NlLw8BqtqjacaJ5MdB3bQokGLlOw7EY/eA5wmpdwvhHACc4UQM8LLxkspn0xgH6dKKX+vspU1wOUfXM47K98B4Jr+1yS1nHAqmK/rocHGyBdC4MzIOCw+v0Qv3St2SYIx7qpibhhid7nIys4+zMMvS+zNNeSX6zoBvz98aoKB553HopkzQ6UL7PZSHn2/ckIqG2Li7BtqSZwdiDuztjyhN4wFuN2XmCpPvpuwyEN8r7omGa4oUYGPMExRGKYoXJmby2xd5+Q4pSOGKgofm0pLDFUUeuXkUKTrNM/O5unRo5NeDiQdrN+1Hndnd0r2XaHQh9N29oc/OsM/suwt6h76Jp13Vr7Dxb0u5oaBN3DGsWek26QKMXfYEXY7f7vuOs6Lk0PfXy3dK7Z/iv+52yoK55sahvwS4+GvKigITWCKaVKyzjBK1ZC/PD+/VHz8b3ffzUV3381yXSdHVSuM0Uc4NibOfmwtibPDnzNrpceLsNk4KrtVueubY/OhypNG3MqT8w2D73WdE2ImcQ1VFD41edU15c3H4wfT4GpE7MsrHTFUUUrZG6lFPyUvLyXlQGqag76DbP1jK12adUnNAcrKuzT/EKqQtISQ4D8W/m4ssAlYBkwBmpWx7UZgEbAQGFnOMUYCC4AFHTp0qHIuaVX42zt/k62eaCUPeg/W6HGryqLCQvnSuHHynYkT5aRx4+TiCnKPlxcWytfGjatUHnqy+LWwUL6UlSUn2O1ygssl8zMy5NN2u3wmK0tuM9nzybhx8iq7XV4J8iq7XX4ybpz8aOJEOXrECPnRxInVsmFjYaHUamku/NSJL8umjiYyw9ZcZmW1l4WFRWWuW1g4X2ZldZZ2ezuZldVZFhbOP2ydSK58a7tdtsvKkkU1dM7zCgvlE+PGRXPyK1q3VVaWbGq3l8rjrwpLCwulkpUlh9jtUsnKSukcklSyYdcGyVjklEVTqrwPqptHL6UMAP2FEE2Bj4QQfYAXgYcJefcPA08B18XZ/AQp5S9CiFbAV0KINVLK2XGOMQmYBDB48OAaeWLwBXzkz8vnw9UfcnW/q1M2EJJM4rUPrGggqo+ipHwQtizamDz8PZs3s2zy5Kh3b25SEltDXmRn8+/wI/n0OXNon5PDoJhzWGIq1VzeNUhlnD0Ryhtw/bV4P/tkFoFgALvXF64PX1Zf2cFo2rum8gWHe/Pfx4SDvtf1cmfuLjIM5uk6w1WVgVW8Rj8YBuea/iana1q53nky6/b3VRRe0rTo30Fd9OaBaFplk8wmKdl/pbJupJR7hBA6cJY0xeaFEJOB6WVs80v49TchxEfAUEKDu2mn/fj27Diwg/5t+vOfE/6TbnMSIp3tA6tKG0WhjaLwi2Gwctq0aLy+nSmMEltDfqau4/WE8sOlx4Oh66WEfolhcINJXP6Tn88fxcUp/Wf/0VST/rgEj1HRgKuqHh9TZKz8+vCKMriUwMeGaSK58ni9ZNnt/LF5M0sNg35x7F1kGFxpuoZvaFqVxD7eIG95wp3suv19FaXOCnyEiNAflXFUSvafSNZNS8AXFvks4HTgMSFEWynlr+HVLgRWxNm2IWCTUu4Lvx8BPJQ886vOo3MfZceBHQB8d813KbvAySa2fWCz7GxeDmcc1PYUs6MVhYs1ja1xYvRQup5M0fLl0ZZ5wWCQZtnZ0a5E2dnN+fKDD9nt8ZMVDHCwpIRHb7oJAFdGRkoG5H40DB4yjSHcr2kJiX1FA66hTkwfmHq4Jt5Xdb5h8DfTTeRDTWOIovChpvFRQQFfT53KJ5Mn88W0aUzUtMPEfl6M0zBP16sk9JUd5B0WHit4v6AAB0Qn+x3JpF3ogbbANCGEHbAB70oppwshXhNC9CcUutkE/BNACHE08LKU8hygNaFQT+RYb0opv0z6WVSBMdoYABaOXFhnRB5Ktw9slp3NE6aJI5OTPHFks2GwUdfprKrVbrMX4WhFOUzg47G7uBibzUYwPINz0eJl3DB6LJ6wly8EIDPpKILYZICscBwyUFKSkgG5eDXpExH6REoZmzsxJdq8A8oO0wxRFJbpOjP9/lKDlLFCPzzGaRiuqiwzDBbqOoNMT0YVzcMYpihMNw3yJhKGcQKfT5uG1+vlk2nTeL2KTxP1hb2eUDGzJhlpCt1IKZcBA+J8f1UZ6/8CnBN+vwHoV00bk857K98D4MkznmRg24FptqbyDFAUBigKL+fllfLIFiRx4shmw2CqKS3y2gqaZycbRVVxZWRERciDI1q1E0DKIHabnRbHdmP3+jVER1ekpHkSqlXGUtnuWxEGKwrvmUoZl58+aXC6KZTytaaVK/YnxNxETjDZNDi8LGJvvLIYAxWFNzQtGqN3ADebnhBe0DRskNA8jIqyZmJJ1tNEfaE2ePT1jndXvQvADYNuSLMl1WNwjEeWzBo3G2PSIjfq1W+eHY+1hsFKXScjO5vi4mIGhL1GL4Izr76ODIL0GaBQtHgtdnsWUh4Me/Q2XBkZ3Pzvu3hq1I3g94cK3tps7C8uTrqdxykK95tq0icao4eQ2Ccy67UyzTuAaJgmXiplP0VhommQMl6MHkJiHxHYqTGpigt1HSeUOQ9jqWEwX9cZUs7+yyLe08SRjCX0KeD4dsfz/qr38fg9ofm+dZT+isJk0z9zVbz5labH8t6m7TvHTHzqnIJ/xLWGwcNuNwc8Hn4PBsEWEu9/5D/DdaP/HW411xAh5uL3+7HZmiBEY4QQ2O2S/Pz7uXbkZez6aS2fPhnKDXA4nfy2eTMrDaPU+SSD4xSlUgIfj4Wm3qyxWUSVbd4B5dfD76colRLgQTFPAYNUFRvEnYexNGYwfHKcMYDyGKgovG56mjiSvXkICb1N2GjgbJCS/R+RQt/uqHYA/LLvF1o2bJlma6qHzfQTyyrDYJmu07eMSUUrDYM7TY/lT2taVBw7KArXalrSY/Sljh+Oe5dEZvgGg/i8Xr764MOoZxsM2pEyVOEwEJAI0QAps5DSRnGxj0WGwSvPPYcEsoSgcSDAF5Mn89W0aTxpOp9kstkw2KDrHFvJ67LQMLjcdL3f0rSo2BvGfHS9iPz85/i9+LeEm3ckk76KwguadliMfrym8WVBQal158eEXubHGQOoCPPTxJHOnpI9HJVxFOHxzKRzxAm9lJIv13+JTdjSUowsmSwzDG4MC0cTu50rr7uOk8O9VVcZBv81icqjcQp/xSuPYBbGDopCB0VhvWHweV4ex6lqwl2eEqF3OO6d6fGwPxhE2Gw4XS7OuOhvvDXHCNcrb0wgIIhMxrbZGgAinIo4gHn6Z9HYvTfchLyJqQZOsoV+s2Hwiin75vpKjF0YMYOnkZRRw5iP232xqbTB+5XKvonHUtMcg8oIcFmpijOmTcPn9TJj2jSe0bRSM7OdLhdDjvDQS3XZuGcjnZp2Stn+jzihX7x9MVOWTOHCHhfSoUmHdJtTLRaEhSMrEKBzIMC8iRNZOG0ad2gay2JEfFmcwl8VlUdYbRjMLihg0dSpBPx+HC4Xd2la0sS+u6Jwn6bFjdF3zMlB12fz+uvrWbVqLVACZNK8eScuuugEBgxoja4voWRPAGGzIYMSaRM4hMAmZYU1cKpKvNo5iQq9EjN4qoTt0/XCmNIGZU+aSoSlhsFI001+UiXDKrEsjvlbWqzrXDVmDJPDDXDMMfpkTMA6Etm4eyM9W/ZM2f6POKF32kJZu+d1Py/NllSfSGZFk5ISRKjOK36Ph7XhcE1ExB12O8XhOu49TP98vRWFpzUtbox+tWEwxu3GVVJCIykRQMDr5Uc98d6tFbHKND4QexNSlOEoynCKip5k1aotQCZwDDt3dmDy5C3YbG/j9/uRMkgjGtGY3diCgp0uF+dfey3n5+amJGxTndo5gxSFtzQtGqMHmJCXR5vsNtFJU3a7g82bf8EwFsSd+ZoIC0zCXFZqZWUYEOMQDAjbHjsGsMgw+Lspbn+kp0wmipSSTXs2cXbXs1N2jCNO6O02e6nXukxk+vcnjz/Olo8/RgIyGCQjO5teisKj4VZ8306dyqzJk/lm2jQe0bTDxD6eIC6LeK7hph8Igd3l4rgEha3INGMzXvGsVYbBv01e5xNl1JS/++4L+fzzVfh8DYDOAAQCPgKBnoSmb+wjUpFfSonf76dxhw4pEXkIhbOu17QqxeghJPaDFIWFhsEVpvOfkD+BosWrmTr1PSZPfotp095H096pktgnklpZGfooCs9oGot1PfrEFcsiw+CZsaF5DjI81nKkp0wmys6DOznkP2SFbpJJywYtybBnMOfnOeT2y023OdWmr6KwbuhQVnz6KY5gEL/Nxu5wemEvRWFluNxvxLtbruulhL7M/Zo81z/sds697jpOzM1NyJsvMgwuNHl2z+bnYysupreq0j28vXl8wFtSwoyCAvZiK9XGzjB+RNdX8PzzV/HwwyvZutUD+IEDQFOgL7AMD0fRmB3YbHacLhdts7N5Ky+PfqpKFrBO1+mmqnROkuhExi6qw7yYeP3B4u106NAevz9QYXXKirABF1x9NUHg3NzcannzEcqrlxT15MMibwuPtRzpKZOJ8vOenwHo2LRjyo5xxAm9L+gDwBPwpNmS5NFbVSEjg5KwB9fb9A+WExNqyEnwn6+nopAXjvX3VVV6liMWPxkGP+o6x6mhln3fm/PBPR6eHDWKTlLicLm4T9Porij0V1XsDgeBQIC9UvLkpFf4/eW3CQQCOBwO/vKXS/jii60EApm4XA5uvfU6Hn98ORAkFMbxA6HB2q49e3P36P+wr/g32mZnMy1cDK2R3U77cN16h8vFrZqWNLGvLsNj4vXDVRUvzpi6N5W3dblhcKvpSeGvual3ZqKTn8Iif8Lpp3P72LGlvPnFhsEPus4wVa31tZlqmk17NgFYHn1V+GHrD6zbtY4rc66MpiwFggEe/u5hPAEPY04ck2YLk0cPReFBTcMoKCCD0lMDeigKj2hatI57PG9+ranhR3fT8p6KUq7AQ0jknzRlodylaZxgyge32Ww0DQQIBoP4vV5W6joHgB90nePOPpuvPv6YjYA3aCMQDDUa8fl8fPzxR0AToDMeT1t0fRtCNEbKA6aj7wZ+ZcOGAxyXMxhF6cdbpkk/zmAQP4CUBLxe1ul6pYR+s2GwSdfplIL00kGKwpumPPJImqWmvVNudcqKWBQzcLpI18lJsbDGTn6KFfl3Jk1i7C23EAwEcGVkUKBpltib2PLHFgDaH9U+Zceol0L/675fOa3gNA76DtKmURtOP/Z0vtv0HbfOuJXlvy1n1JBRKR3hTgcZwMpp0/B7vSyaNo1RJu+1h6JEBT4yEzUSRllrGIwzCfU9YY87UX40ZaFEBmvPGTOGj8IzNjP27EF7+mmCgMvlwpadzVWnnorP68XucHDAbkcGAoSaIZoJpVQKESAY7EVRUZBQUCKyngR+BXbj89l4cOyTPDD2ZvqZBg59djsOIQj6/dhdLrpVIpSw2TB41VQC4poE0yh/MoyEG5JH4vVmYqtTVpaBMQOnA2sgfFLe5KfFhsHYUaPwh7uFeT0efjBVXF1qqq2TjBBTXeT3g7/jsDlomtk0Zceod0IvpeSmz2/ikO8QAGe8Vrpb1K1Db+WZs55Jh2kpZX2M4K6P471GZqJGRP2+cFlgc7Gu1bpeKaE/LhwaighiZLB2qKLQGBjjduMNBrHZ7fwjP5+5ixfj8XgQhDz3nv36Ubx8OcGgn0D0WUQQCs8IevQYwpo1WUjZGPARis9Hcuq3ADaCQT9ff/0xc+d8wFeaxmOaFu0+VdUY/aaYEhCbEkij/MkwGG+6vndoGh4od9JasslRFJ7TNBbpOgNVNeXefISyJj/9oOsEwvWJAGx2O8NMs2tvNIWZXqpmGmhdZeeBnWRnZadsshTUQ6G//cvb+eTHT3js9Mdw2pw8MucRjmtxHIVbCrlz+J3knZ6X0guaLrrGCG7XOJ7cyhhRj3j25hh+z5jtVhlGuS37uigKd2laqRh9hEguvwwGkUKwp7iYyL+8DP8sWrGCiy+/ljff+hBb0Aa2owgGWwACm60hp5zSkx9/DCJlpHhqNlCCw+FkwoQxfPjBy3z99cfI4H68Xjvf6Tr/HTOmlK1lCfx6U8gqdpC5U0wJiE4JeMZrY262ekEBn4QnGpU1aS0V5ChKjQl8RQxTVTIyMvB6PNhsNsY+/3zUm18Ykwa6sJppoHWVVb+volt2t5Qeo94J/XNFzwHw7+P/jRCCO5Q70mxRzXAI6HL11QRFCZ/0+YkDXRoftk6sqEfCN/eYGn6YvflVhsF/TB7XY2UI1SGgOPxqpm9MGKGvqtIVeHXy5NBALRCQkvfemUGAY7A7bdxxx7U895wenSU6YMCxhLpRSkIDsS5sNhcTJgxi5Mgu5OS4mDvnA7xeOy6Xi1MSDFWsNwweM3nfV+bns7+4mB5h0e+gKFyjaZWK0XePudmWQIWT1iqiyDBIV4/X8uY5rDCMctMtIwxQFAo0Le5AbLzaOkcagWCAJduXcMPA1BZYrHdCn2HP4Jaht9RLr70sVoUnN3m9Hhbkwu87g/xz+j+ZeO5EjC0G09dN55jGx3BM42M4btpt7Fu7lb79j8fftQm/HfiNY4cNiRuuWRozsLc0jlCtMAxuN90MnjGVsS0rcyfvhRe45eab8QaDOIXAF8wkGJQIAU2bNkTT8tD1ZahqXwoKfiMYPEQolBNCSli8eAs33fQeUMLT+S+wq/hXTqlEfRhzyMrn8VAwahQyPKP27vDs38qmUXZRFO7QtGiM3gPMNHn0fRMQMsOYh65/h6qegh3JX01pqp9qWo2JfXnzHFYYBqNNy/LLKF0cIVJWO5Z+4XkgsTH6FYYRDT2lqwVmTfFj8Y8c9B1kUNtBKT1OvRL6Q75DeAIesrOy021Kylhu+ieIPJ4v03W8Pg87ugT5PZyKW7ilkJwXc6LbZWdlU3zIVL53wRuhVuxhmmQ0oVlWM5plNou+0tLLuhEC+wFBptfGrz0CfLPxG1o2aEmLBi3IbpAdd3q8+Z8zXuZOt5wcfA5HKKxjs2MTTmz+ILCfoqJvUdU+jBlzKYbxK1On6kCAUIZNqCmDlEEmTnyBUOsDicu1C11/HUVJ/J/F3KNW2GwEAwEIZwatqcbs3y6KUip8dWN+PjNeeYWmRx9NsJztICTybveZUWG/4eorommqJSUlvFFQUGNCH68OUkToK/qdV4bY2bUrDIPbTDeRZyu4icTyoynZoLqVRlPBuuJ1vLL4FU7scCLndj+XGetmADDkmOrVNqqIeiX0u0t2A9A8q3maLUkNsTnSz379Ndl9OrL82J18+f/+lJGrO17GtJ/fBuAu5S4eOvUhspxZlPhL+P3g7/x+8HeKDxZH3+88uJPdh3azu2Q3uw7tYnfJblbtXBX6rNjwBv2Aj38tuw+WlbapsaMR/luCOA6Aq0TyQYu5LP/qblo0aEGLBi2iN4UWDVrQsmFLmmQ0Qdf1cPkCSTAY4NzzBvLZZ7MJBPx8/PE3fPbZx7zwwnMUF/fD74+c117gDyALWE+oi2Xoqc3ns4cnFyUu9F0Vhf+EQ1aNs7N5c/ToaAihR5JCCKsNgwm33speb6j65twZM3ju22/LFC5d/65UPfoAAofDgT8QQErJpFdew4sgN/eqlFe2LK8OUlklEZJBvPTQRIX+R8NgrCkcNzbBdo81gcfv4aHvHuKx7x8jIAM8bTzN9QOuZ+qSqZzW+TR6tOiR0uPXK6HfdWgXUH+FPvaf4Mqvr2HFV+tKrdM6oyVTr36TlwJTyXRkllqW6cik3VHtomWaE+WQ71D0JhC9ORzYGX2/bvNqtuzYSKCRjYX7l/HlD1qZE9KyHFk0dzYneI2EPwQcEOw49hcCvQ7AIQGbnAT8NkaNuoMJEz7Cbj9IMLgHKbPCe9hFqOzBnxUtpTxEdnblW7B1VZSo594uJ4c1uh6N0SeDZbpOic8X/eyvwPtV1VNK1aO/MjeX7dt/Y/rHn+DHRsAXYOLEl5k27VW0CrpPVZdeisITpjpI5pBdH0Uhv4KSCFWlOumh8ZINaoPQL92+lNyPc1m2YxnX9r+Wfx//b27/8nZeWvgSp3Q8hXcvfjflNtRLoa/r5YfLYqCq4nA52dNcsv7CIAdlSOR7tezF5PMmk2HPoF+bfgghDhP56pDlzCLLmcXRjY9OaH0pJQd9B9l5cGepG8POgzv5dd+vbNu3jTWZa9i8ezP7bfsxgt/BReGN5x8NnzcnECjhlVdeJxi0IWXEm+9IaDasJNSOuBjYAByguPi3ap2jWfSTRV9VJdPpxBP26B0VeL+KMhxNmxmN0Qsks774HA92QgPRPqQM4PUGK+w+lQx6KUqZg8fllUSoDn0UhWdN6aGVOUa8ZIN0ss+zj7H6WJ4reo7sBtl8dvlnnNv9XABmXTULb8CLy+6qEVvqldDvKdkDEIov1zOklLxQXMDs/5SU+n7bndsSFuCaQghBQ1dDGroaVjitW0rJH54/uHrCbXyyvwB2HAe0R8rFzJ//C1I2AlYRErothAqb2QgNzu4GfgYE2dlNU3hGVaOnovCkrjOzoIAAcGZuboXCFanaCfBEXh4BvxcH4Rm+Yex2e0Ldp6pak76yrDSl4CajmFy8m0i8puWxHKcojA2XvU53jH7+tvlc/sHlbNi9gStyruCZs54hu0HpscOaEnmop0Kfyhlm6eLTHz/lpYUvRT/P+vsszuhyRjlb1A2EEDTJbEJ/z8V8UvIOHP0b0BRsAtn4d9gbgOgwZhDYBtFW4KGJVTabj9WLF/BS3m+1rpZKImUkyuIkVcVut0Ow9DDuddddV6E3v9Qw+KdpPGdiiiYjrTQM7jIdJxVdvZYZBv9UVQI+H3ank4m6Xq7YpztcM2nhJG754hbaNGrD7Gtnc2KHE9NqD8TvQFdn2X0oNBhb34T+twO/ccE7FwDQp1Uf/Pf564XIm+mvZIPDDw0PYbM3RVz+M9yxCFr/OegaIuKbSCCAEA1pbBN8MHkyj/+//8fVbjeLDaPmTyAFDFcUnnn+eTJspf9NBw4YUOG28WrSp4J4KbjLDYNpeXksT9Lv4fOCgmjJ7IDXy+cxbQ1rC0EZ5I4v7+Cf0/+J+1g3S25cUitEHuqZ0Ec8+iaZlR+Yq20c8h1ivDGe6z65jtZPto5+v2jkonpRSz+WNc7Z4Ahg1zsiT/wa2W1vaIGyA2hIKFTTgJDQhwZhhQhy0fluHFLyRyDArmCQA+FaKnUdwzDIy8ujR04O5/z1r9gBO+AEli9eXOH2kZr0drs9KTXpyyJSW8hmD5WIbpSdza1uN5Puu49b3e6kiP2u7duBP4ffI59rE/6gn+s/vZ78H/K5behtTL98eq1KCqlXoZvdJbtp7GqMw1Z3T8sf9DN18VQe/O5Btu3bFv2+a/OurB61uk6fW3ls37+dDDLxNV+PPHULYlkT2rdsxeacdYivs5D7WxPySw4RkrwAdruXY9q0JChldD9+my1aS6WuYhgGp4cnSjnsdoKBQKlnmqkvv8yVubkMKydE0U9RmKhpKY/R91YUnjTVFlqgJ796Zos2baLnLwj99t/NyyOngvLZNYU/6Oey9y/jg9UfMPaUsdx/yv21bsJmvfPo62rYJiiDvLPiHXpN6MXI6SNp36Q9o4eNBkJpkStvXllvRR7AF/DhoYTgRVtgaxYZszpxQ98bwAbOIVtxia2E/sVDxc7gEFJ68ZNBRkZGqNmF08kjEybUqhh9VXizoABRUoIIBPD5Qr1k4c8Alj8QYHYCTy39FIXrx4xJef2Y3orCFWPG0FtRoumREQ8/GdUzz8nNDVU+FQKX08mKL77g9fvu4163m9W1IEz3v9n/44PVH/D0iKd5QH2g1ok81DOPfk/JnjqXWimlZMb6Gdz7zb0s2b6EPq368Mlln3Be9/MQQvCv4//FMY2PqZV/PMnkwp4X8sKCF8AGYzo/xHmzTsfBIf43wYZ3cJCWxi7+8GfiCTRCykPYbIFwrvkV5OZezFxd58Q01INJNvMNgw+mTiVDSjKAQ3Y7ASnxh716G6Gsm5OrIKCJ1qepDqmonpmjKLyo6yzSdXZv3sw3kyeX6piWTq/e2GLwv9n/I7dfbq2uq1Wh0AshMoHZhEqeO4D3pZQPCCHGAjcAO8Or3iOl/CLO9mcBzxByx16WUj6aJNsPY3fJ7jrl0c/dPJcx2hjmbp5L56adee3C17i8z+WlYvCVndxUV3F3dvPMWc9wZpczOa7FcQCMz8sjcx54roIDPX1c1HEIvYeeQnZ2M4qLd4ebc4Rmw9Z1gY/wva4T8Pmij9p/PfdcPpg+vVT5hKAQBIEFhkGhrnO8qjK4gvOvbH2a6pCK6pmRfa42DGaH+y5UpmNaKtjn2cdVH11F+ybtee7s59JmRyIk4tF7gNOklPuFEE5grhBiRnjZeCnlk2VtKISwAxOAM4CtwHwhxKdSylXVNTwee0r2pLQdV7JYsn0J935zL1+s+4I2jdrwwjkvcP3A62s0r7a2IYTgtmG3lfruRFUl60EH+3d4KRkGs17+mCZtWqKqqS8BkC6aZ2dDMBgV+p27dkWbdkBoMNLv9/N2QQGfmgqmvadp5Yp9MuvTpJOeSumOaeny5n0BH7d9eRsb92xEv1rnqIyj0mJHolQo9FJKCewPf3SGf2TZW5RiKLBeSrkBQAjxNnA+oRkwSWf3od0MaFNx6ll19v/Lvl844DvAAe+BMl8P+g6G3pexfN2udTTLbMaj7ke5dditNHA2SJnNdZkhisLfr72OiT+8xL6/ws5jgrw4cTJTp73GK/lP4S0urnZ4YKFhHNbOz8xy00Sdmqjxvru4GJvNFu2/GiwpPUFOAA6Hgx8XLcLr8RAMBsHrpVDXyxX6VNanqWmqMzchGezYv4NhLw/j570/M+bEMZzU8aS02ZIoCcXow575QqArMEFK+YMQ4mzgFiFELqE6iP+SUu6O2fQYQtMZI2wFhpVxjJHASIAOHTpU6iQi7Pfu57Vlr/H2ircRQiAQ2IQt+j7eq03YDvsOQrHzCA6bA7vNzua9myu0QSBo4GwQmhnqbFjqtXlWcxq6GvL3vn/ntmG31akwU7q4NDeXd994lf0HS5DDgsifBaIE/nvTfTThAM0zHuY5TcML5Qp2PBYaBleaygC/oWmltl1uGIwyhTsmaFrKxf54VcWVkRE95tXXX8/CJUso8fmwCcGJJ57ImnnzWLFgQfRm4HS5OL4c4Y405r45P58DxcUpjdHXd6SU/HP6P9m+fztvX/Q2/9f7/9JtUkIkJPRSygDQXwjRFPhICNEHeBF4mJB3/zDwFHBdzKbxRhDjPg1IKScBkwAGDx6c6BNDKZ4/53mW7ViGlBKJLPUalMHDvitrmQibLYQITdKQAXxBH/6gn8FtB9Mtu9thIh55zXJk1fuB05pksKLwwcxvuOnDUSzosRjRLItDu9uxWQpsNKe3ZwufFBTw+rRpZQp2PNYaBlPGjo16xT6vl3m6Xmq7eB2QUi30QxSF9zUtGnsfoij0yMmJNh+Zr+us/f57AsEgdpuNk04/nX+NHVumN7/YMMg13cwKUhibPxJ4a8VbfPLjJzxxxhNc2ufSdJuTMJXKupFS7hFC6MBZ5ti8EGIyMD3OJlsBc2vzdsAvVbAzIa7IuYIrcq5I1e4t0sRgReGDXh9z7DPH0v7i9mx+2Ra6QQP7bY3xAl5T/Dki2D+YujOZc87XGgYPud3s9nhC8fCwVzw8xiuuTAek5aaMlureDIYoCkNM+xiuKAwPf3YATpcLwh5/eSIPoZ6t5mtjbsydbLuTzTrDYJWu00tV6VYLbNu+fzu3zriV4e2Gc8fw2pthE49Esm5aAr6wyGcBpwOPCSHaylBRcIALgRVxNp8PdBNCdCZUpOQywFJii0rToUkHLux5ITPts3A1OhbfAT92u+DGO25E/rEdh8NBAKKC/YNhcJ7Jk/1M06JiHyln2zAYpKfNRqfTT+e6sWMPewrIURQmmDoglSWEy+M0y0iVaA5WFN7RNAxdR0kg22aYquIyxebNk8lq0u7Kss4wGGeqLX+PpqVV7KWU3Pz5zRzwHmDq+VPr3Oz0RDz6tsC0cJzeBrwrpZwuhHhNCNGfUChmE/BPACHE0YTSKM+RUvqFELcAMwmlV06RUq5MwXlYHAFc0ecK3l/1PhPfGU3xEi8ds7N4Z/RIfF4vHex2TrjhBv6am8sgReHJvLxSTTzm6HpU6M3lbJu5XNw+dmzcVopQdqrgItMg7oo4GS1VEUzzPgeWs/1gRalQ4COU17M1XiZObRH6VTG15VfpesJCn4r5Au+ufJeP1nzEY6c/lvImIakgkaybZcBhqSxSyqvKWP8X4BzT5y+Aw/LrLSwqS1CGssmVgQPJOTuHt/PyokLlAnp26BD1yk8Ke7IRj/4kkyfbXVG431TOtiyRL4tFhsHfTU8LD+XnVzujZVGcgeHyxL4ylNWztTZn4vSKqS3fK0HbUjFfYNehXdwy4xaGHjOUO5U7q7WvdFGvZsZa1G/2ekKFziJF6/rGCJW5+fYwReEzTYsbo4eQ2FdW4CPMC8e9fYEA3pISFi5ezLOmjktV8YrnxcTS5+l60oS+LHLCTT7KsnulYUQ7TCW79HBFdFMU7tG0SsfoUzFf4B7tHnYf2s3k3Ml1twyJlLLW/QwaNEhaWMTydOHTkrHI3Yd2R79bWVgo3xo3Tq4sLKwxOxYWFsquLpdsBbIVyPYZGbKomsdfWFgoe2RlyS52u+yRlSUX1uD5xGNFYaEckZUlT7Xb5YisLLkizfYkyvLCQunOypIn2+3SnZUll1fT7qKtRVKMFXL0jNFJsjB1AAtkGZpaR29PFkcif3j+AKCxq3H0u/La3aWKgYrC3667jmkTJyKlxO/3U6jrpTJlqrLPNzQtoRh9TbAkxjNeous17tVXhWT2sw0EA9z0+U20adSGB099MIlW1jyW0FvUGfZ69tLI1ahWZDxcnJvL26YSBOVNWEqUgYpSIwK/0DCiWTtlzTfoHxMW61+L4vcVkax+tpMWTmLhrwt5829v1voSBxVhCb1FnWFvyV6aZNSOpjLxJjbVBRYaBpebBivfKmNyWW9F4WlNS1uMPt3s2L+De765h9M6n8ZlfS5LtznVxhJ6izrDH94/2LZvGwt/WYjD5sBhc+Cyu3DanbjsLrIcWWQ6Msl0ZNbI7OTYiU1VZYVp0DPVs1aNcEgmEAiA14sRMxvYTG9FSbnArzaMtBcoi8eds+7koO8gL5zzQr2Y6W4JvUWdQYbrDw2ePLjCdTPsGWQ5Q8IfuQFU+Dnme5fdhdPmxGl34rQ5ozeVyHfm5R2bdKR1o9YV2hXLCsPgDpOHPT7FJQqUcEgmMrNWSWNIZrVhcK9pUtQjmlYrxP6bjd/w5vI3uf/k+6Mls+s6ltBb1Ble+MsLXDfgOgLBAP6gH1/Qhy/gwxvw4gl48Pg9HPIfosRfwiFf+NUf8xr+fnfJ7rjrlfhLKjYkDg2dDVly4xK6Nu9aqe3iDXqmUugHKQpvmWbWJloALhUsj5kUle4mIhCaq/Hvr/5NxyYdGXPSmLTakkwsobeoM7Rq2Ipzup1T8YrVICiDeANeDvkO4Q14ozcTXzB0Q4m8j9xgfEEfB7wHuPrjq7ltxm18fsXn0Uf9Hw0jOinruFo06DlIURISePMMUwnRUhBBqHYv2jWGQfHmzdjtoYH1dDcRifD+qvdZ9OsiCi4oINORmW5zkoYl9BYWJmzCFo3zV4ZNezZx56w7+eTHT7igxwX8aBg8aApLPKBpccW+j6Iw3jToWVsqS5pnmNodDnzhdoY2ux2/EPj9fpwuFxM1rVyxj1eOYI1h8ED42jgdDs644QZOzc0t05tfZxis1nV6pri4mT/o5/998//o06pPvSuOaAm9hUUSuHXYrUxdMpXbv7ydM449I1o4LRKWWKnrZXr1yUoHTCbmGaYyGCQABKXEH34vpcTv9bJA18sU+rLKEZivDUDrDh3KFfk80w1zTJziZqsNg2W6Tt9qDui+v+p91u1ax4eXfFgrUniTia3iVSwsLCrCYXMw4ZwJbN67mXFzxkULp9nsdhwuF42ys/kgL481hpHwPpcaBq/k5bG0Etski0gdnIj9dqcTu92Oy+nE6XJhD38/uLyGJ3HKEcCfReWEzYaw2TgqO7vMfayOuWGuDu8DQqGxl266iTGqSsF99zHG7WZ1Fa+VlJJH5z5KzxY9Ob/H+VXaR23G8ugtLJLESR1PIrdfLk8UPkHuTbk8EC6c1ig7m1dGj456pQ9qGj0q8DyXGgb/NHnDFYVIkk3sDNOqxOjLKprWQ1H4R34+r4waBYEAr48eTcecnLi1h3rGFDfrGd7Hj+Hwj7ekBBHOxvJ5vSyr4oDuzJ9msnTHUqaePxWbqH/+ryX0FhZJ5PHTH+e1pa/RY0IPgvcHOU5R+CAv77AwjlnoF5hmqkbKDy+I6W5VXogkVcSGlMxFzxKxpbxyBAeKi7FLSTAYjJYhjif03RSFMZqGVlCAB/CGv18R9vQJi7xdCGwxhe0qw6NzH6XdUe3qXWw+giX0FhZJpHWj1vRu1ZsVv63gHu0e8k7PK1X/3uFy0dskRgsMg0tMnvu7mhaqNx/T3aq8EEltpqzxh8qUIfYCH4XLTXwxbRpPaxoNs7NDoR8pcTqdnHzttZxWzoBueRRtK+K7n79j/Jnjcdldld6+LmAJvYVFkllwwwIyH8nktWWvce/J99JDUXjQVP/e7M3Hm6k6WFHopyhM1DQW6DpHZWczT9cJQty68jVBsmfvdlcU7jWVIS6vZHTsXINZBQV8M20agUAAp83GDc8+y1kjR1bZlhfmv0AjVyOuH3B9lfdR27GE3sIiyWQ4Mii8rpDjpxzPw989zGNnPEYPRYkbly9vpmo/RSEIXGNqSPKqptW42Kdq9m6iPQFi5xrYCMXjg8EgQSHYXVxcZRt2HdrFOyvf4dr+19I4o3HFG9RRLKG3sEgBSnuF6/pfx9Pznuaa/tfQs2XPuOsNVhTejekBu9IwWKrr9FNVimIakhTFNPeuCWp69m4ssQXWBPC1qXJoP9PN0RvwsnnvZg54D9CjRQ8yHBnl7nvakmmU+Eu4cfCNqT2JNGMJvYVFinj09Ef5cM2H3DLjFr6+6usyi2OZe8CuNAzuMnnPI/PzSzX3HppArH6pYTBf1xlSjZmrZmpDyeLYAmuPaRpLdZ0eJylsafkHL35xK1/+9CU/7foJSWiA1mlzMnLQSJ4a8VRcwZdS8tLClzi+/fH0bd23xs4lHVhCb2GRIlo2bMm408Zx8xc38+7Kd7m0z6UVbrM0xns+VFzMq5pGka4zNKa5d9ztDYMbTKGeyUlIy6xts3e379+OkbGK6ccW8dXsRzjgO0CWIwv3sW6uzLmSTk07keXI4usNXzNh/gQ+W/sZD5zyAFf3u7rURKh5W+extngt9550bxrPpmawhN7CIoWMHDSSlxe/zJ2z7uScbudUGAfuF+M99wvXgk80XDM/JtQzP0lpmbVh9u5vB35jrD6WSQsnEZAB2h/Vnqv7Xc253c9F7aSS5cwqtf6lfS7lsj6XMUYbw/WfhgZarxtwXXT5rJ9mIRCc2/3cGj2PdGAJvYVFCrHb7LxwzgsMf2U4D373IE+OeLLc9XsrCk+GwxIRka8MQ1S1VKhnSB1NyzQjpeRp42ke/O5BDvkPcdPgmxg5aCR9WvWpsFa8+1g3P3T+gcxHMlm9c3WpZV9t+IrBRw+meVbzVJpfKxCRGt+1icGDB8sFCxak2wwLi6Rxw6c3MHXJVJbftLzMgdlkkewYfTrRNmjcMuMW1vy+hnO7n8tTI56ie3b3Su/nxCknsqdkDytuXgHAPs8+mj/enH8f/2/Guccl2+y0IIRYKKWM26yh/s31tbCohTzifoSGrob8a9a/Un6sforCP8aMqRMiv9IweDMvj5VxatS8veJtzn7jbKSUTD1/Kp9e9mmVRB7gop4XsXLnSn7Z9wsQmiTlD/o5peMp1bK/rmAJvYVFDdCqYSvuP/l+ZqyfwYx1M9JtTq0gkmE05b77uMvtLiX2z8x7hss/uBylvcK8f8zjmv7XVKul3wkdTgBC3aMACrcUAjD0mKHVOIO6gyX0FhY1xK3DbqVb827cOetOfAFfwtstNwxezctjeRqqWKaS2AyjpeHKlG8se4PRM0dzYY8Lmfn3mTTNbFrtYw0+ejBdmnXhkTmPMH3tdJ4tepYTO5xIs6xm1d53XaBCoRdCZAohioQQS4UQK4UQD8Ysv0sIIYUQLcrYfpMQYrkQYokQwgq8WxyxuOwunhrxFGt+X8OLC15MaJvlhsEot5uJ993HKLe7lNgXGQZP5+VRVIM3gGXhm86yJBwzkmFks9ujGUZLti/hH5/9g5M7nszbF7+dtC5PNmHjxb+8yIbdGzjvrfNw2By8fN7LSdl3XSCRrBsPcJqUcr8QwgnMFULMkFLOE0K0B84ANlewj1OllL9X11gLi7rOud3P5Yxjz2CsPpYrc64ku0HZtdghVBrYXMVyoa6ToygUGQYXmPLlP9Y0hqY4Jr8sfNOJZPRM0DT6VvGYK8O1c0bl5/NHcTH9VJWj+/dg8OTBZGdl897/vZf0AmNndDmDNaPWsOjXRZza+dQjItsmQoUevQyxP/zRGf6JpOqMB+42fbawsCgHIQTjzxzPH54/eEB/oML1B4W93kijj0HhdMm54Xz5QCCA1+tlrqkhR6pYFHPTWVTFY640DP4Vjs0/P3o0/VSVnsOHkftxLlv2buG9/3uPVg1bJdf4MJ2bdeaiXhcdUSIPCebRCyHswEKgKzBBSvmDEOKvwDYp5dIKBkkkMEsIIYGJUspJZRxjJDASoEOHDpU4BQuLukXvVr3556B/8tKCl7hp8E1k/PRHtCdq1xgPOUdRmKBp0aYfkZrwJ4bz5SMe/Yk1kC8/UFVx2u3IYBBhtzOwiseMrZ2z8FuNl/e+y/S103nu7OdQ2tf+bKG6RkJCL6UMAP2FEE2Bj4QQfYF7gREJbH6ClPIXIUQr4CshxBop5ew4x5gETIJQHn2iJ2BhURd58NQHeXPFm9z47nUcd/cyAl4fDpeL/2haXLHPifluqKLwsaYxV9c5UVVTHrYBsANZQuADnEJQ1a6q5to5NHbyXKMPWfDDYm4ZcgujhoxKosUWESqVdSOl3APowPlAZ2CpEGIT0A5YJIRoE2ebX8KvvwEfAUdGPpOFRTm0aNCCB055gLnFRfzcIX5P1IoYqijcOWZMjYg8hDzxgN8PUhLw+1lSxdBNb0XhKU3joof+w6/3dWDp3hVMu2Aaz53zXLVSKC3KJpGsm5ZhTx4hRBZwOrBYStlKStlJStkJ2AoMlFJuj9m2oRCiceQ9oSeAFck9BQuLusnNQ26mc4MOzB8hwWkr1RO1NtI/JkumOlUsj+rVjmeOep9Nh7bwyWWfkNsvN3mGWhxGIqGbtsC0cJzeBrwrpZxe1spCiKOBl6WU5wCtCYV6Isd6U0r5ZfXNtrCo+7jsLp47/wXOfetcXPefw3/c9x4WtqlNRDzxSBXLytbhibDr0C7OfP1Mtu/fzqyrZnFihxOTbKlFLFatGwuLNCKl5Kw3zqJoWxHrb11fYbplXeeQ7xBnvHYG83+Zz8y/z0TtpKbbpHqDVevGwqKWIoTg6RFPs8+zj7H62HSbk1ICwQB//+jvFG4p5LULX7NEvgaxhN7CIs30btWbGwffyIsLXmTVzlXpNidl3P3V3Xy4+kPGnzmeS3pfkm5zjigsobewqAWMVcfSOKMxd868k9oYTq0us3+ezdPznubmwTdz+/Db023OEYcl9BYWtYBIuuXMn2YyY33dqW65wjB4PS+PFeXUvinxl3DDZzfQqWknHj/j8Rq0ziKC1WHKwqKWMGrIKF5a8BJ3zryTM449A6fdmW6TWGJqYtI/JstmhWFwp6n2zdOaFrfd4P9m/4+1xWuZ+feZNHQ1rCnTLUxYHr2FRS3BaXfy1Iin+LH4RybMn1Duuol40tVliWHwD7eb5+67j3+43SyJOVZsKYN4E6iW7VjGY98/Rm6/XEZ0SWQivUUqsITewqIWcU63czizy5mM1cey88DOuOusMAzucLt5+b77uMPtTpnYx2s0bqaiCVSBYICbPr+JpplNeXrE0ymx0SIxLKG3sKhFRKpbHvAd4L5v74u7TiKedDKINBq3h4U8ttF4H0Xh9vx8hrrd3J6ff1jY5n+z/0fhlkKeGvFUvZ8fUNuxYvQWFrWMni17csuQW3jmh2e4cfCN9G/Tv9Ryc1GwiCe91DBYoOsMTmJD8P6KwsuaVmaM/otJk5gwahQEg6ydM4djc3LoGV5n16FdPGk8ycW9LrbKG9QCrJmxFha1kD0le+j2XDd6teyFfrV+WLGvFeHGHf1VlQBwg2lQdLKmpbwx+CrD4I5TTiHgC7VEdNlsXPO//3HJmDEAPPTdQzygP8DSG5fSt3XflNpiEcKaGWthUcdomtmUR057hNk/z+b9Ve8ftryPovD3MWPooygsiGkIsqAGmpAs0XWCgcCfX9hs5IRDO1v2buHJwic5/7jzLZGvJVhCb2FRS7l+wPX0a92Pu766i4O+g2WuNzimC9XgFFbAXGEYPHXTTawuKsJut4MQ2Ox2bpkwgZ6KgpSSyz+4HInk6TOtAdjaghWjt7Copdhtdp49+1lOefUUnix8kvtPuT/uev0UhcmalvQYfSwrDIPbTj0Vn8cTsg8QhAaQO+bkALDglwV8v+V7nj/7eY5tdmxK7LCoPJZHb2FRizm548lc0vsSHp37KJv3bi5zvX6KwvVjxqQ0Nr9Y1/F7vdHPgfCPx+9nZkEBAM/Pf55MRyZX9r0yZXZYVB5L6C0sajmPn/44Esl/vv5PpbZbaBg8n5fHwiTl2Q9QVRwuV/SzjVBIwEaoMbSUks/Xfs5lfS6jaWbTpBzTIjlYQm9hUcvp2LQj/znhP7y94m3m/DwnoW0WGgaXut08cd99XOp2J0Xs+ygKz377LRfceCODTj6ZTMAJZADHDRjAgl8WUHyomJM6nFTtY1kkF0voLSzqAHefcDftjmrH7V/eTiAYqHB9I5yJEwhPqjKSlInTR1H414sv0qVXr1B8PvyzcfFiXln8Cg2cDfhbz78l5VgWycMSeguLOkADZwOeOOMJFm9fzNQlUytcXzFl4jhdLpQqZuIsNwxezctjecwTQfH27aFwTfhzkCDaRo1TO51qhW1qIVbWjYVFHeHS3pcyYf4E7tHu4eJeF5crqIMUhXc0DUPXUVSVQVUYpF1uGIwyTcSaoGnkKAorDIO5M2YQIOQpZjoc7Dq1CetXr+ch9aEqn59F6rA8eguLOoIQgmfOeobfD/7Ow989XOH6gxSFW8aMqZLIAyyMmYi1MBz+WaLrBPx+gkBACE7/xz/4eNdMhh4zlMv6XFalY1mkFkvoLSzqEAPbDuT6AdfzbNGzrPl9TUqPNShmItagcPjHXLXSlZlJ30vOZtmOZVzS65LDSjVY1A6sWjcWFnWM3w78RrfnunFC+xP44sovUnqs5YbBQl1nkKqSY3oyMNfaWdv0Vy569yLmXT+PYe2GpdQei7Ipr9aNFaO3sKhjtGrYigdOeYB/zfoXn6/9nL90/0vKjpWjKKUEPkIfRYmWJX796//itDnp16ZfyuywqB5W6MbCog5yy9BbOC77OO6YeQfegLfiDVJI0bYi+rfpT6YjM612WJSNJfQWFnUQl93F+DPHs27XOp794dm02REIBpj/y3yGHjM0bTZYVIwl9BYWdZSzu53NOd3O4aHvHmLH/h1psWHN72vY791vCX0tp0KhF0JkCiGKhBBLhRArhRAPxiy/SwghhRAtytj+LCHEj0KI9UKI/ybLcAsLCxh/5ngO+Q9xj3ZPWo5ftK0IgGHHWIOwtZlEPHoPcJqUsh/QHzhLCDEcQAjRHjgDiFtWTwhhByYAZwO9gMuFEL2SYLeFhQXQPbs7tw+7nalLprLgl5rPVPth2w80yWhCt+xuNX5si8SpUOhliP3hj87wTyQnczxwt+lzLEOB9VLKDVJKL/A2cH71TLawsDBz38n30bJhS27/8nZqOl26aFsRQ44Zgk1YUeDaTEK/HSGEXQixBPgN+EpK+YMQ4q/ANinl0nI2PQbYYvq8NfxdvGOMFEIsEEIs2LlzZ2LWW1hY0CSzCeNOG0fhlkLeWvFWjR33oO8gy3Yss8I2dYCEhF5KGZBS9gfaAUOFEH2Be4H4LW/+JN40ubguh5RykpRysJRycMuWLRMxy8LCIsw1/a9hYNuB3P3V3RzwHih33VWGwdt5eayqZunixb8uJiAD1kBsHaBSz1tSyj2ATij80hlYKoTYROgGsEgI0SZmk61Ae9PndsAvVbTVwsKiDOw2O8+e9Szb9m3j0bmPlrneKsPgv2430+67j/+63dUS+8hArCX0tZ9Esm5aCiGaht9nAacDi6WUraSUnaSUnQgJ+kAp5faYzecD3YQQnYUQLuAy4NNknoCFhUWIEzqcwOV9LueJwifYtGdT3HWWmQqV+bxelpVRp365YTAtTnliM2t+X0PLBi1p0yjWv7OobSRSAqEtMC2cQWMD3pVSTi9rZSHE0cDLUspzpJR+IcQtwExCvYSnSClXJsNwCwuLw3ns9Mf4eM3H3DXrLh5u/y+W6zo5qkrPcLmCvuGCZJHSw33j1KlfbhjcYipP/K/8fPYVF9NfVeltKoewcc9Gjm58dE2dmkU1qFDopZTLgAEVrNPJ9P4X4BzT5y+A1FZesrCwAKB9k/aMOXEM9+v3s/3+z2i2PoDD5WKcptFTUeilKDyqaSzTdfqqKr3i1LFZZPL6Ax4Pz9xyCzIYxO5wcM611zIiN5duQwfx/ZbvuX7A9Wk4S4vKYuVEWVjUM+46/i5a0JRlp3nxy1At+eWmEE0vReGyMWPiijzAQFN5YrvNRjAQCIV6PB4+mziRf7vdvDNzCgd9Bzm106k1dFYW1cESeguLekaWM4t7+t7FvtawbaDA4XKRU4lWgjmKwvOaxsiHH+ZfEybgysj4s868lPi8Xr5cFBpqO7HDiSk4A4tkY5UptrCoh4y+4B5e2/wOG87dyJSnP47G6GPZfWg32kaNFb+twOP34Al4sAs7zbOac/4/zqdXy150yclhZkEBM6dMIRgI4HS5ONBa0PaPtrRsaKVC1wUsobewqIcIIZjwt4kcP+V4pgfmMBh3dFnxwWJeXPAin6/7nKJtRQRlEACnzUmGI4OgDHLQd5B7vrmH49sfzwOnPMCdL77Imbm5LNV1+qkqI1ffxXEtjkvX6VlUEqvDlIVFPeaS9y7h83Wfs/7W9bRo0IKnjaf535z/ccB7gGHthjHi2BGM6DKCoccMxWl3Rrf77cBvvLb0NR4vfByP38Mv//qFBs4G0eWDJg2iVcNWzLhyRjpOyyIOVocpC4sjlDx3Hh+v+Rh3gRubsLFy50rOP+58HjntEXq36l3mdq0atuJfx/+LwUcPRp2mMmnhJEYPHx1d3jSzKfs8+1J/AhZJwRqMtbCox3Rp3oWbh9zM6t9Xs9ezl08v+5SPL/u4XJE3c3LHkzmr61nc/dXdfPXTV9HvHTYH/qA/VWZbJBlL6C0s6jmPnPYIr/z1FVaPWs15x51XqW2FELx10Vu0atiKh2c/HP3eZXfhCXiSbapFirCE3sKintPQ1ZDrBlxHI1ejKm3fNLMpJ3c8mS1/hArRSinZsHsDrRq2SqaZFinEEnoLC4sKGdR2EJv2bGLRr4v4ZuM3rNq5igt7XJhusywSxMq6sbCwqJAd+3cwcNJAdh3aRSAYoN1R7Vh+03Iauhqm2zSLMFbWjYWFRbVo3ag1c6+dy6NzH6WBswH/PuHflsjXISyht7CwSIjOzToz8byJ6TbDogpYMXoLCwuLeo4l9BYWFhb1HEvoLSwsLOo5ltBbWFhY1HMsobewsLCo51hCb2FhYVHPsYTewsLCop5jCb2FhYVFPadWlkAQQuwEfq6BQ7UAfq+B49QVrOtRGut6lMa6HodTm65JRyll3N6OtVLoawohxIKyakMciVjXozTW9SiNdT0Op65cEyt0Y2FhYVHPsYTewsLCop5zpAv9pHQbUMuwrkdprOtRGut6HE6duCZHdIzewsLC4kjgSPfoLSwsLOo9ltBbWFhY1HOOOKEXQvQXQswTQiwRQiwQQgyNWd5BCLFfCHFXumysacq6JkKIM4QQC4UQy8Ovp6Xb1pqgvL8RIcQYIcR6IcSPQogz02lnTSGEeCd8LZYIITYJIZaEv3cKIaaF/z5WCyHGpNnUGqGs6xFe1lcIYQghVoavS2YaTf0TKeUR9QPMAs4Ovz8H0GOWfwC8B9yVblvTfU2AAcDR4fd9gG3ptjXN16MXsBTIADoDPwH2dNtbw9fmKeD+8PsrgLfD7xsAm4BO6bYxjdfDASwD+oU/Z9eWv48jsZWgBI4Kv28C/BJZIIS4ANgAHKh5s9JK3GsipVxsWmclkCmEyJBSemrYvpqmrL+R8wkJmwfYKIRYDwwFjJo3seYRQgjgEiDyZCeBhkIIB5AFeIE/0mRejRPneowAlkkplwJIKYvTZVssR6LQjwZmCiGeJBS6Oh5ACNEQ+A9wBnDEhG3CjCbONYnhImDxESDyUPb1OAaYZ1pva/i7I4WTgB1SynXhz+8Tuvn9Ssijv0NKuStdxqWB2OvRHZBCiJlAS0JOweNps85EvRR6IcTXQJs4i+4F3IT+ID8QQlwCvAKcDjwIjJdS7g/dqOsXVbwmkW17A48R8ljqBVW8HvH+MOpFfnJ510NK+Un4/eXAW6ZlQ4EAcDTQDJgjhPhaSrkhpcbWAFW8Hg7gRGAIcBDQhBALpZRaSo1NgCMuj14IsRdoKqWU4UevvVLKo4QQc4D24dWaAkFCsbfn02RqjVHWNQkvawd8A1wrpfw+nXbWFOX8jYwBkFLmhdebCYyVUtb70E04PLMNGCSl3Br+bgIwT0r5WvjzFOBLKeW76bO0ZijjelwGnCWlvCb8+T6gREr5RNoMDXPEZd0QireeEn5/GrAOQEp5kpSyk5SyE5APjDsSRD5M3GsihGgKfA6MOVJEPkzc6wF8ClwmhMgQQnQGugFFabAvHZwOrImIWpjNwGkiRENgOLAmLdbVPPGux0ygrxCiQfhGcAqwKi3WxVAvQzcVcAPwTPgXUQKMTLM9tYGyrsktQFfgvrB3AjBCSvlbGmysSeJeDynlSiHEu4T+ef3AKCllIH1m1iiXUTpMATABmAqsIBTWmiqlXFbThqWJw66HlHK3EOJpYD6hkN4XUsrP02FcLEdc6MbCwsLiSONIDN1YWFhYHFFYQm9hYWFRz7GE3sLCwqKeYwm9hYWFRT3HEnoLCwuLeo4l9BYWFhb1HEvoLSwsLOo5/x/D/G5BVbCSYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vA9YBv8g5LjMzK5Msp4Y2AY8A/xgRl+ccj5mZlVmWq4beAHwduFjSQ5K+LunPco7LzMzKJEsbwWrgNuCrwM9IuqD+u/7Wk3SrpGclre1l+VxJuyStSh+fGGDsZmY2CLK0EawARgAPkrQNnBkRGzPU/TXgJpKjid7cHxHnZajLzMxykqWNYF5EbBtoxRFxn6SpAw/JzMzKKcupoQEngQGYI2m1pCWSpvVWSNJ8SSskrdi2Lc9wzMyqT6ZO53KyEpgSEa8DPg/c2VvBiLg5IloioqWxsbFc8ZmZVYWKJYKIeKFroJt0TOR6SRMqFY+ZWbXqtY1A0vl9rRgR3z2UDUuaCDwTESFpFklS8ljIZmZl1ldj8dvTv8cCZ5BcOgrwFmAp0GcikLQYmAtMkLQZ+CRQDxARC4ALgA9I6gD2AhdFRBzUqzAzs4PWayKIiEsBJN0FvDoitqbTk4Av9FdxRPQ5wH1E3ERyeamZmVVQljaCqV1JIPUMcHJO8ZiZWZlluY9gqaQfA4uBAC4C7s01KjMzK5t+E0FEXCnpnbw8YP3NEXFHvmGZmVm5ZDkigOSa/90R8RNJoySNiYjdeQZmZmbl0W8bgaQ/B/4T+HI663j6uPnLzMwOL1kai68gGZjmBYCI+BXJJaVmZjYMZEkE+yKirWtCUh1Jo7GZmQ0DWRLBzyVdAxwh6W3Ad4Af5BuWmZmVS5ZEcDWwjWTw+r8A7gY+nmdQZmZWPlkuHy0AX0kfZmY2zPTV6dwa+mgLiIjX5hKRmZmVVV9HBB5C0sysCvTV6VyWcYnNzOww19epod2UPjUkICLiqNyiMjOzsunriGBMOQMxM7PK6OuI4KiIeEHSMaWWR8Rz+YVlZmbl0ldj8SKSBuNWklNEKloWQHOOcZmZWZn0lQiuT/+eGhG/LUcwZmZWfn3dWfy59O+D5QjEzMwqo68jgnZJXwUmS7qx58KIuCq/sMzMrFz6u6HsrcBZJO0EZmY2DPV1+eh24JuSHo+I1WWMyczMyijLUJVb0m6opxaXj4j35xWUmZmVT5ZE8D3gfuAnQGe+4ZiZWbllSQSjIuKjA61Y0q0k7QzPRsT0EstFcmXSOcAe4H0RsXKg2zEzs0OTJRHcJemciLh7gHV/DbgJ+Hovy+cBJ6WP04EvpX/NKmrR8k0sWbuVedMncfHpTQC0btzJsg07mN08nplTxu03DXD7ys1s372PCWNGMP24sezc09a9rFQ5gAljRvCuGZO767t95WYEnD9jMgA3LHmc9dte4pWNo5nRNI6fPP4MezsKHD92JGNHNbBrTxtbnt/L7rYO9rUV6IwChQJ0lughTAy/8WUFHFFfw572Qvf0K8aM4Mgj6jlmVD27f9vR/X8YNaLugP/PuFENLH3iWTZse5HmxiP5izf/DpD8j365cSc797Rx8ivGEFDyvTBuVEN3PWu37GL9M7vZ11HgwtOauPj0Jlo37uS7KzcT0P1/7k2p91fxusB+ywd9X0b0/fZIO58bDewD2hlAp3OSpgJ39XJE8GVgaUQsTqefAOZGxNa+6mxpaYkVK1b0t2mzg7Jo+SauuWNN9/Q/vvM1nDJxDJfcsoy2jgINdTV84rxpXHfXOto6CtTV1lAoFOgo7F9PjaCutgYi6ChEr+UaasW1fzida7+/lrb0G7y+VnQWgsJw++YeQkolxtoaIeKA/1GX4vfCvvZCd3cLpf5Nl5/ZzK0PPkVbWllDrVg8f07JL/HWjTsPeH9d+4N13evW1UBNTQ0dncnyhZfNPqhkIKk1IlpKLcsyQllenc8dDzxdNL05nXdAIpA0H5gP0NTUlFM4ZrBk7dYDpnfuaaOto0AhoL2jwJK1W/ebLvVF0LUMki+K3sq1dwZL1m6lvehnfHupn/Q2qErt4c5+Mm/xe6GrZG9r/Gjdb7r//5D8T5dt2FHyC3zZhh0HvL+K1+0ogAqF7vdRb/Ucin7HLJZ0ZqnHIGxbJeaV3K8RcXNEtERES2Nj4yBs2qy0edMnHTA9u3k8DXU11Arq62qYN33SftN1JT5FNemy+lr1Wa6+VsybPon6Wu03r6bUp8MGTandW1ujkv+jLsXvha5ivf2bzp42kfqiyupr1X16sKdS76/idetqkvldy3ur51BkaSP4P0XPRwKzSG4wO+sQt70ZOKFoejKw5RDrNDskXeeBe7YRLLxs9n7naE+ZOGZQ2whOmTjGbQQDVKk2gq73Qn9tBG+bNjFTG8HMKeNKvr+GVBvBAStIJwD/HBHvzlB2Kr23EZwLXEly1dDpwI0RMau/Ot1GYGY2cIfURlDCZuCAL/YSG10MzAUmSNoMfBKoB4iIBcDdJElgPcnlo5ceRCxmZnaI+k0Ekj7Py0eVNcDrgX67nOjviCGSQ5Er+g/RzMzylOWIoPg8TAewOCIeyCkeMzMrsyyXj95WjkDMzKwy+r181MzMhjcnAjOzKtdrIpD0H+nfD5UvHDMzK7e+jghmSpoCvF/SOEnHFD/KFaCZmeWrr8biBcCPgGaSO4mL76aOdL6ZmR3mej0iiIgbI+JU4NaIaI6IE4seTgJmZsNElstHPyDpdcDvprPui4hH8w3LzMzKJUvvo1cBC4Fj08dCSR/MOzAzMyuPLHcWXwacHhEvAUi6AXgI+HyegZmZWXlkuY9A7D9ofSe9d8NtZmaHmSxHBF8Flku6I53+I+Dfc4vIzMzKKktj8b9KWgq8ieRI4NKI+GXegZmZWXlkGo8gIlYCK3OOxczMKsB9DZmZVTknAjOzKtdnIpBUK+kn5QrGzMzKr89EEBGdwB5JY8sUj5mZlVmWxuLfAmsk3QO81DUzIq7KLSozMyubLIngh+nDzMyGoUxjFks6AmiKiCfKEJOZmZVRlk7n3g6sIhmbAEmvl/T9nOMyM7MyyXL56LXALOB5gIhYBZyYW0RmZlZWWRJBR0Ts6jEvslQu6WxJT0haL+nqEsvnStolaVX6+ESWes3MbPBkaSxeK+lioFbSScBVwIP9rSSpFvgC8DZgM/CIpO9HxGM9it4fEecNMG4zMxskWY4IPghMA/YBi4EXgA9nWG8WsD4iNkREG/BN4B0HGaeZmeUky1VDe4C/TQekiYjYnbHu44Gni6Y3A6eXKDdH0mpgC/CRiFjXs4Ck+cB8gKampoybNzOzLLJcNXSapDXAoyQ3lq2WNDND3aUGr+nZtrASmBIRryMZ8ezOUhVFxM0R0RIRLY2NjRk2bWZmWWU5NfTvwF9GxNSImApcQTJYTX82AycUTU8m+dXfLSJeiIgX0+d3A/WSJmQJ3MzMBkeWRLA7Iu7vmoiIXwBZTg89Apwk6URJDcBFwH73H0iaKEnp81lpPDuyBm9mZoeu1zYCSTPSpw9L+jJJQ3EAFwJL+6s4IjokXQn8GKgFbo2IdZIuT5cvAC4APiCpA9gLXBQRmS5NNTOzwaHevncl3dvHehERZ+UTUt9aWlpixYoVldi0mdlhS1JrRLSUWtbrEUFEvCW/kMzMbKjo9/JRSUcDfwpMLS7vbqjNzIaHLHcW3w0sA9YAhXzDMTOzcsuSCEZGxF/lHomZmVVElstH/0PSn0uaJOmYrkfukZmZWVlkOSJoAz4N/C0v3xkcQHNeQZmZWflkSQR/BbwyIrbnHYyZmZVfllND64A9eQdiZmaVkeWIoBNYld5gtq9rpi8fNTMbHrIkgjvppVdQMzM7/GUZj+C2cgRiZmaVkeXO4icpMUZxRPiqITOzYSDLqaHiTopGAn8M+D4CM7Nhot+rhiJiR9Hj1xHxWaAiPY+amdngy3JqaEbRZA3JEcKY3CIyM7OyynJq6F+KnncATwF/kks0ZmZWdlmuGvK4BGZmw1iWU0MjgHdx4HgE1+UXlpmZlUuWU0PfA3YBrRTdWWxmZsNDlkQwOSLOzj0SMzOriCydzj0o6TW5R2JmZhWR5YjgTcD70juM9wECIiJem2tkZmZWFlkSwbzcozAzs4rJcvnoxnIEYmZmlZGljeCgSTpb0hOS1ku6usRySboxXf5oj7uYzcysDLKcGjookmqBLwBvAzYDj0j6fkQ8VlRsHnBS+jgd+FL6d9BNvfqH3c+fuv7cPDZhxqLlm/jWI5to6yiw5YW9tLUHk48eye59HYyoq6W+robmCaP5bXsnjzz1HLUSY0fVc9TIetoLwYt729m+p406iVENtTy/t51Cj75/62ugvVCZ13e4EFAjeM3xY2moq2HtlhfoLAQ1QEcEI2prOKKhlhCMqK1h2nFjaZ4wmoc27OAVR41k7inHsnNPG7v3tvPQhh2MqKth7KgGjh0zgmnHjWXtll2sf2Y3+zoKzGkez8pNO1n/7Iu88tgj+aM3TGbdll1s272PCWNGMP24sazbsosA3jVjMjOnjAOgdeNOlm3Ywezm8X3OK8v+ijigh+nBqViaA1wbEX+QTn8MICL+qajMl4GlEbE4nX4CmBsRW3urt6WlJVasWDGgWIqTQBcnAxtsi5Zv4po71lQ6DBvCGmrF4vlzALjklmW0dRRoqKth4WWzS84bzGQgqTUiWkoty+2IADgeeLpoejMH/tovVeZ4YL9EIGk+MB+gqalp0AM1GwxL1vb6+8UMgPbOYNmGHQC0dRQoBLR3FHqdV66jgjzbCFRiXs/DjyxliIibI6IlIloaGxsHJTizwTZv+qRKh2BDXH2tmN08ntnN42moq6FWUF9X0+u8csnziGAzcELR9GRgy0GUOWRPXX+u2wgsdxefnhytuo2g8g6HNoKFl80+oD2g1Lyy7K8c2wjqgP8Gfg/4NfAIcHFErCsqcy5wJXAOyWmjGyNiVl/1HkwbgZlZtatIG0FEdEi6EvgxUAvcGhHrJF2eLl8A3E2SBNYDe4BL84rHzMxKy/PUEBFxN8mXffG8BUXPA7gizxjMzKxvud5QZmZmQ58TgZlZlXMiMDOrck4EZmZVLrfLR/MiaRtwsD2iTgC2D2I4eXGcg+twiPNwiBEc52ArZ5xTIqLkHbmHXSI4FJJW9HYd7VDiOAfX4RDn4RAjOM7BNlTi9KkhM7Mq50RgZlblqi0R3FzpADJynIPrcIjzcIgRHOdgGxJxVlUbgZmZHajajgjMzKwHJwIzsyo3LBOBpLMlPSFpvaSrSyyXpBvT5Y9KmjFE47wkje9RSQ9Ket1QjLOo3GmSOiVdUM740m33G6OkuZJWSVon6efljjGNob//+VhJP5C0Oo2z7D3ySrpV0rOS1vayfKh8fvqLc6h8fvqMs6hcxT4/RMSwepB0ef0/QDPQAKwGXt2jzDnAEpLxK2YDy4donGcA49Ln84ZqnEXlfkbS2+wFQy1G4GjgMaApnT52KO5L4BrghvR5I/Ac0FDmOM8EZgBre1le8c9Pxjgr/vnJEmfRe6Min5+IGJZHBLOA9RGxISLagG8C7+hR5h3A1yOxDDhaUrnHGew3zoh4MCJ2ppPLSEZwK7cs+xPgg8DtwLPlDC6VJcaLge9GxCaAiBiqcQYwRpKAI0kSQUc5g4yI+9Lt9mYofH76jXOIfH6y7E+o7OdnWCaC44Gni6Y3p/MGWiZvA43hz0h+hZVbv3FKOh54J7CAysiyL08GxklaKqlV0p+WLbqXZYnzJuBUkiFb1wAfioihNjDlUPj8DFSlPj/9GgKfn3wHpqkQlZjX8xrZLGXyljkGSW8heSO/KdeISssS52eBj0ZEZ/JDtuyyxFgHzCQZOvUI4CFJyyLiv/MOrkiWOP8AWAWcBfwOcI+k+yPihZxjG4ih8PnJrMKfnyw+S2U/P8MyEWwGTiiankzy62qgZfKWKQZJrwVuAeZFxI4yxVYsS5wtwDfTN/EE4BxJHRFxZ1kizP4/3x4RLwEvSboPeB3JuNrlkiXOS4HrIzlxvF7Sk8CrgIfLE2ImQ+Hzk8kQ+PxkUenPz7BsLK4DNgAn8nKD3LQeZc5l/8auh4donE0k4zmfMZT3Z4/yX6P8jcVZ9uWpwE/TsqOAtcD0IRjnl4Br0+evAH4NTKjA/30qvTfCVvzzkzHOin9+ssTZo1zZPz8RMfyOCCKiQ9KVwI9JWuJvjYh1ki5Ply8gaZk/h+RNsofkV9hQjPMTwHjgi+mvhY4oc0+FGeOsqCwxRsTjkn4EPAoUgFsios/L+SoRJ/Ap4GuS1pB80X40IsranbKkxcBcYIKkzcAngfqiGCv++ckYZ8U/PxnjrDh3MWFmVuWG41VDZmY2AE4EZmZVzonAzKzKORGYmVU5JwIzsyrnRGC5kHS0pL8cxPrmSjpjsOobziS9XtI5Ay0n6Q/76l3Whi8nAsvL0UDJRCCp9iDqm0vSm6T17/Uk1/kPqFxEfD8irs8pJhvCnAgsL9cDv5P2///p9Bf9vZIWkXSmhqQ70w7g1kma37Vi2mf/yrRP/p9KmgpcDvzvtL7fLd6QpGslfaRoeq2kqZJGS/phWs9aSRemy2dK+nm67R/37DkzHRPgKUk16fQoSU9Lqpd0laTH0j7uv9nfTpD0HkkPp3F/WVJt2u/8o5JGpjGukzQ93Uf3Sboj3caCohh+X9JD6X75jqQj0/mnKelrf3W6nbHAdcCF6TYvlDQrLfPL9O8pkhpKlHufpJvSeqek+/7R9G9TOv9rSsYieFDSBlWi73wbfJW+9dqP4fmgxy31JL/oXwJOLJp3TPr3CJIuH8aT9MH/dFe5ojLXAh/pZVv7LUvrmgq8C/hK0fyxJHd0Pgg0pvMuJLnDt2ed3wPeUlTmlvT5FmBE+vzofvbBqcAPgPp0+ovAn6bP/wH4DPAF4GNF++i3JOMV1AL3ABeQ9D9zHzA6LfdRkrtmG0i6rDgtnX8USTcW7wNuKorjKKAuff5W4Pb0ec9y3dNp3P8rff5+4M70+deA75D8iHw1SbfaFX+/+XFoj2HXxYQNaQ9HxJNF01dJemf6/ATgJJJEcF9XuYjorx/3vqwBPiPpBuCuiLhf0nRgOkmvnpB84W4tse63SBLAvcBFJF/ikHRRsVDSncCd/Wz/90h6PH0k3dYRvNzf/HXAIyRf/FcVrfNwRGyA7q4J3pSWeTXwQFpPA/AQcAqwNSIeAYi0h1Id2IPlWOA2SSeR9BJa30/cAHOA89Pn/wH8c9GyOyPpGvsxSa/IUJcNcU4EVk4vdT2RNJfk1+mciNgjaSkwkqR/nYH2e9LB/qc5RwJExH9LmklyHvyfJP0XcAewLiLm9FPn99N1jiH5Mv9ZOv9ckhGn/hD4O0nTIqK3gWME3BYRHyux7BiSgWfq03i79k3P1x5pPfdExLv3qzzpWTPLvvoUcG9EvDM9zbY0wzo9FW9nX3EYB1GXDTFuI7C87AbG9LF8LLAzTQKvIunFEpJfum+WdCJA+kXcX31PkQwFiJLxc7vWPQ7YExHfIDkNMwN4AmiUNCctUy9pWs8KI+JFkq6fP0dyNNGZnq8/ISLuBf6GpEH8yD5e40+BCyQd2/VaJE1Jl90M/B2wELihaJ1Zkk5Mt3Uh8AuS0bXeKOmVaT2jJJ0M/D/gOEmnpfPHSKorsa/GkvRiCsnpny597dMHSY6EAC5J47BhyonAchFJ3+8PpI20ny5R5EdAnaRHSX6xLkvX2wbMB74raTXJKRpIzlm/s1RjMckQf8dIWgV8gJfHGHgN8HA6/2+Bf4hkiMgLgBvS+lfR+9VI3wLeUxRDLfANJT2D/hL4t4h4XlKLpFtK7IPHgI8D/5W+znuASUpGR+uIiEUkjeqnSTorXe2hdN5a4EngjnSfvA9YnNazDHhV+louBD6fvpZ7SI4u7gVe3dUITHJa558kPZC+hi49yxW7Crg03d57gQ/1so9sGHDvo2ZDRHq67CMRcV6FQ7Eq4yMCM7Mq5yMCM7Mq5yMCM7Mq50RgZlblnAjMzKqcE4GZWZVzIjAzq3L/HxFo3XxruJW9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11023027071557448 = NC std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12332271433169224 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.7222\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00292 0.50976 HD avg vs true 0.50684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.15673, should have been 0.16204\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for IL\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 1.409\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.885 NC seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the NC map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  745669.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jnv0dVcQzdituVmvYbBQ2GUQrZ2577whJbzfgl0p4fUcnNy+qpWZMVcbYhANzJ53Klr3vo7iROKsjrRzDdMc0Z1Fcs1LHke7ATLVpdxdPtewKFF13Ms3TG/cgIXtTKg2ptKsVwjWC0qp84/ENnDm1uujPsxBRJQDwxu7sGkNhWnZ0suyH62nb937G++5JaWzmsuBWLE2llW8+voGPnDU1MB/957rtwa4jrfD9l95h6fmzgmvz+UqGc98CUwSGcQJESxB/6cozAFjV3GuDvnTB5KBSZlrh9xfXMnPC6EAJ+P6DqeNOAXrDQROer8Ffrfr+gVXN7bTsyCyT8OEzp/LCpr19lt8Bxo2p5KA3waaBzXu6+Men3wrq+Efx9FsGgePbcd+jf1618ERdalJKzoY0sYTMV/61z2/ayyPLLuLu6xsy/BiptPKI56CPOpJXNrcjuLWNOo50s/Pg0V6FWiDpbrAxRWAYJ0CuqpXhY5t2dwUTUVph3Cj3zy4ar/6Zi+Zk3HvHwWM4AufPqeHV9k633WLC4dF12zIyaSsc2HvoWGwLRp8bzpvBO/sPZ+QdgDvxH4yssv3a//nwLECBHOHIpWJIOMWP7Q/hBLTY50tmJFWYOGt5d0pZ2dwe5CN88/ENwfW+gvYn9qgJKvzMIAoMsnZ1Q4kpAsM4QeLMAf6xh9duY8Xqt4PjAjzw0juk1fUf+KvunmSa1l2HgpW1T9ozCX3+0nl0HU+ytu1ARm19AEXYsKMzb3z9ke4Uy6+tZ+n9L8c6VPuKun7sogm/L0dg2vjR7Og4esJy5KLQW8yjM3Pyo/XbA58JkhmeFI4O23HwaKxSDh9yxA0hHi6YIjCMEhF1/oIf4+6XV3ajT1Q1I+oknBvgjnJt0QmJt+37UUVRJRKmbf9hVjW383t1E2jedjCjJER/UIpf0U+rHsXurl5HrgglVQKlIplSVjW389i67Vmf38frp3HXEy2uY9/J/1044objDqeQUlMEhlEiYtsmOkJCJHAWhyN0Fk6r5qE7lrCquT3L/JNOK9F51/Hu509KiYSrVOIm6Hf2v8+Wve+74yQ76aqUhJUAwORTR+WM8Okr08aNYs+h41nRT34+gSMwYXR2pFF/eaplV+yO6jdv7g2UtP/5OxD4dsKcM7O3Q1o4v2IoQ01NERhGiYg6fwE0rdxywawMZ7HvJ/AdjjctqmVf13E27uxkZ6cbM++HpLoVOYVPNs5iX9fxIOpFcHMHNkScyACTT82s3pnPLFLItn6iOAI3nDeT+1a35TwfzYTOt7qOKgHI7EWQVrj0jMlF+T1GVzoc7cm9zVHgvcPxCuXQsWTWsVx38p3n4SgiP7y4mBLepcAUgTGsiYu7HinFvD5/2Xye27Q3sBe7YYlCw4zx3H6hWzzx3ue2ZGSnfuepN1j/bkdGk5Wrzp7K5QunZMX237bi5V67uyPMnTSWlp2HsrydcSWcc1HqXcJ1586genQlly6YFBveuuySzMb0QF6zV9yxqNnm1e0Hi5ItnxKIctrYKt473D8bfzKV3e8Asp3Og4kpAmPYMty6RvVVAS2eXcNfX9fAUy27mDi2iidfdytr3v1kKwunVWdUE3XLOWe3VVTgmY17eP6tfUG9Gz+hLOyQTKU176o336oasnyf/aIYc9MTr+0krW5fgPGjK+g8mrmSfubNvby7//2s605EtLrTxvDugfgKpf2lv0oAwJHeMt3hSrJ+1vhQ+A5MERjDlkJdo7p70tzzzFt86cozSq4M+pIM5CuMrqM9OSOEwoXb/FIEv27dnRXeCa6Jwc8l8K+tGVPVp8kxbC6JYyCUwLJL5rGyuT3vDsQXIZnSLCXgCzIQYaWXLphE59EeqiocmrZ2FL5gkHAE7rh4blaYsfkIDCMHS+ZNDBKqEonMrlHdPWnSwG+37Gfdu+/FNnbp6wo+3/g1bQeKSgaK2n190qokvHDLXIXbasZU8Vr7hqx7VjjgeCUcRISuoz2saTtQcJXvI7jdup57c++AhI7GkVYK1goqhhkTRvPO/sP9Cu8M89KW/ThCUAYjF2dNqy55wtu0caO44byZPPDSOyTTyvdfbGP15n1UJhwumjeR6tGVwQ5xqDBFYAxvwimquOaW5dfUs2L122w9cCTYLXz3qTdYv7UD1d56OX2pB19oxR9egaeB17YfzKr7H61IGcXxnLy+aSe62/FrDj3Vsov66ePoOp4MfAKbdncF7RtzOVqjBOYegeM9KaaOP2VIY/eLIV9ZjL7glpMuPK6QEhg/uoKFU6vpOpbMGJtwIJ0uThHve/84h44ngx1ZSmHjLvde/g7wlMq+tx0dSEwRGMOWNW0HemvtpN3MzpXN7Vm1+hF4JWRbPx4pw1youYj/rHw9BDqOdGfYwJ/euIfVm/dlNW33G7zEKYN0Wpk5YXRw33Bpiq6jPXz639ZSP30cS+ZNzJJzTdsB0hpf8iEXYR06UBNsudF5NJnxu+UIXHmW67z/pqeYC5FKwyttB/Ka5/zf0R971U0rE8Jd1zUMmrnIFIExbAk7UuOatfhEbcoiZJQEDjcXERE+fOYU7rxsfsYfV7gFpF+fP7zS981UfnlmxVU4969uC+TxC7b5dt+uoz18/8W2wMwRNm/5pSlWNbezeU9XsMp/cfP+2DDCaIvK4cKk6ir2dw2fDNm+0J9cirTC5OpRdBzpJlfl5jiT3ZZ9hwPTYNz4ygqH/V3Hg1yE7pRb8E5hUIIiTBEYw5ZwHZ98zVqiLPSqXd68qBYF9ncd7w1HVLfi5LMb9/A3N54TlBiuGVPFpQsm8+wbe0illeU/3RAUT0skhA8vnEI6nalxsmQRN1Rx58Gj1M8Yz5q2AxljLj9jMuCGjNaMqaJlZyc/bmrP6imgwLGeNPe/8DbnzpoQOBJ9p3LX0R7uf7HthB28A8FHz57Gw2uzE+dGAv1VqHu73NyFhCOo38gmdH7quPiEuVhzocCtF9Rx86JaVnl9HXz8BcRghJRaPwJjRODb8HuSbheqYiJLHC+DNqW5o2ISBaJpToToyjCaCdzXe/m7hHy9CPI9vxScNa2aTXu6htUuZaCZM3EMWw8ccSd/cXd2cTvTXIj3ryIh9KQ163fxKx9ze1I0be3gtu+7v+MVCXET69Ju+ZGB2BFYPwJjxBPeHew4eLS33n8einEYlkoJQPYknIZ+L0N9U9TdP2ulp8j4ysGYm9/Y3UVCiM0JOFnY5ikBIOjJ0BcU11z5Rx+aC5Dl7PerkC6eXcMjn+utWguD5zg2RWAMOcWGefoVPZu2drCquZ1jfcgEHUoGamWuEJtnMNSklJNWCUBmqYh8ejz6PYeT9NIK969u46qzp2aFrLbu7P1Oo5VsBytyqJQ9i38AXAPsVdWGmPMC/BPwCeAI8BlVbS6VPMbwIlxsq69hnmvaDvDx+mlF1Y8ZDhSrBM6aVs2s08b0rYmKUTIE1z+ULCKpocJx23K27uzkMa9gYEJApdcUqMQ3yPGLE4Z7Vw82pdwRPAj8M/DDHOevBhZ4/y4E/tX73zjJCcfsh3v6RjNuozuE8HVDaZKucPInKvW3sufmve+zcFp1cL0I1NWM4XgyNWDVOo1eTp9yKtsOHCaZyq7seumCSShuIb9wZFgcjsDd1zdw+4V13PvcFjfIADfPYN7kMby973De65NpeGjtNlZGupwNJk6pbqyqq4H38gy5HvihuqwBJojI9FLJYwwfwjH7aa/Be0IyM27/4deb+NQDazLKA4SvG8oYh0Im4sbZNZw+5dSi7hWOKEx69YJ8JXL9uTPY1Xk0UAJ96AOTwZiqRD+v7Bv9lW+oeHvv+6QUpteMzjq3evN+Xtq8n++/mF8JgFtawy8i6If5Orgmpbb9h4tu4HPMK5kS/p1v2trBvc9tKXmZjKH0EcwEtodet3vHdkUHisgyYBlAXV3doAhnlI5on18/LDKamRtt/7fj4NGMHr7DEQGat3UUFdVUkRDu+NDcICEt+p7WtB3IaERTM6aScaMr+1xArWSrvQjD9TvJhZ+ouDNHtrVS3M7u0PFe/4gf1HDPM2/x2y37g/LSxe4SX9qcWTJlsAosDqUiiNOTsR+Vqq4AVoAbPlpKoYzSk6vPr09YSdSMqeLrP9kQJIRVOML8KacGTVaGG0rhHYNP/fRxXFU/javqp3HPM2/x0ub9GX8A0Yim94709KvByvvdqT5fU07kmlD8CbyQiyA6kS2eXcOXrjyDte+8F5gxE47wkYVT+M2be/MX/8ONDrvvhbc51pPqrW9V4lyCoVQE7cCs0OtaYGR4/4wTJq7Pr398+TX1Qb2du59szWjd2JNSThtTmbHCijZTEdyG7+tCdf1LzemTx2b1Ei7Ea+2dLF3xMh9eOIVTKhNUegoQ3PfSlz4CADNrRjNuVEXO+jnn1o5nw47OkzrmfyCZMeEU1yyXxw5ZmRBu8vsYh1g8u4ZbFtfyyFo3CVLTynmzJnDFwikZje/jUAgaDvmktbTN7odSETwBfFFEHsV1EneqapZZyCgvmrZ2BFFEL799IMtkomTWFQJ34r/1wrogfM9v3PLdp94YNGXQ36qeyZQGkSQOMHti/2vn7+g4yo4c5wRYfm09m3Z38ZePbzBlUAQ7Dh7Lee70yWO5cN7EvJE+frawv7sN+gz0I57YobTN7ksZPvoIcDkwSUTagb8CKgFU9T7gF7iho1tww0c/WypZjJFDZvXO3ubuEL9F9+u03Bz6g/QzNP3EH0dg3qSxtO0/3KcJcEp1FXMmjs1SPHEMROOT9ADdJw7fYXn7hXX8n5ffLXnp5ZOdeZNP5W9vPCfvmLAJtGZMVZAMWWy/BV9fCG7l2hG5I1DV2wqcV+ALpXq+MTLJ5UiuGVPF8p+2ZKy8xasEeedl8wG3ho/vcO4JGerTCmNHVZBwhHREmyyYPJYjyXRseea9Xd0cONxDRZGx5MMZVXeHtKPzWElLUY9kBNcclG8n4PObN/fwjZ9sKBj775/znb4VCYdEAb9DwhE+d/FcqkdXZjQ3Cne2G2gss9gYVhRyJGcoA4UXN+/jioVTuOuJlozyvdFEoA07OmNNvZv3HSaRJ6xmIEtQDEbtn1zEmdTKkQljKjkY43B3xE0K29tVXL5GMg0PFxn7H97lplJpPnLW1LxJgyJwVf00Fs+u8fISNCuKbqAZrMgywyiaxbNr+MIVp2clk3Uc6ebu6xu4ZMEk10GM+8fx2LptdKdcX0J3Snlu014czxYi4k7AfpJPHAPRGhG8Ind5zvdHCQhuctPkU0tnFhhqplWPGrRnxSmBCsfdWdZNHJvRB/rUUfnzL/zfv5XN7Xlj/f1drl+wbs+h/DuOVEpZ03aApq0d7Dx4lAqnN8+mVP2MbUdgDHui3cOWX1Mf9BqorHCYOu4UoLdeyzv7D5P0ZnfVwhVGB2KlfvqUU5k7aSzPvrFnQJf9itujIHpLwc0r6E846XBjd5Gr8FKRTMOzMWGdH5w/iec37c3I5fArifoxo+FeF/li/W9aVMv+ruM8v2kvG3bkrxflh00vvf93JNNuN7SlXqnqUoWP2o7AGPZEk8z85i9//tGFPHTHEi5fOCVj/Dv73ifhhKK7VfNm+g7EvL1l7/v85s29vRPFABInnwKXev0NjGyiq/mKAsnVUSWQcGDPoWOcN2tC8H06Ah+oHU9FhYMqOCJcvnAKyVQ6q0SKj7+IefSVbfzmzb30pDRnwEJC4KNnT+Wua+v5wUttQT5KKg1b9rjO/VJlGduOwBj2RB3Ivu/AXx1F//jSCuNHVQSr5aBDmDNwZqA4SlnSOo517+ar4HLiCHDl2VN5Z9/7fc6RGGreP56ZRJfsQ06d33XNr/Tq9yeucIT6mW4uhgKqyqTqUVm/m2GKjYI7t3Y8y6+tB1zHcrSy7rp33Ui4QjuP/mKKwBj2FHIgh9s4grtajppMhioTORohkpD40gUJR0jHlJnIRzS6pVgTV7EKUYHNe7qoO23MiFMEhRhV4XA8Rwp4OvLlBJ+VCA0zxmdM/DcvquXmRbVF/W46ItzhRQPVjKnir7wAB0dg6fl1gXM4rt+B74/w/x9op7EpAmNEkCsT2eeTi2vZ13WctgFcvd556Tze3n+YZ9/Y0+8ErMWza1i/tSOoObN4dg0KrA8lulVVONx1bT3Pb9p7QiWoEw7UzxhfsGdBX3ZF7x44UrLchqEklxIA6Dwa73dJJjN7Uocn/ly/m36m/HKv0f2DL7/LQ3csYdPurmAHmVa464kWFk6rpst7tps74O5EFLculSNCKhW/8zhRTBEYI5qwI7nCkYwVcYUDHz5zKs/0YyIXoHp0Jd//w0a+/pMN/e7L6ysByAzh9GWbXD0qKFHwVMuJJdarQv3M8Wza0zVimvbA0IbVxqFp11b/1p6uDCXoOJJllixE09YOnmrZFWTI+1FG/7lue8bvZE9Kuf+FtzMWAtd+YAa/2LDLjWRS5fKFU4Lfl4F2Gpuz2BjRZDiSUxrkDgjudnvFHzbykbOmZlwzc8IpBR26Ce+PHtxSAadUOsE1ApxS6fDtG89hZkwJ4zC5FFAyDefOmhBkp37qgTX8dsv+4P4JIdPhXSQNM8bz0B1LuOrsqXnHCTD7tDED5tgWoCohVFU4fbqn0L/32Rfy3b4yIVmToOMIn79sPv/w++dxSqWD430XV5w5JfYeEF8u2l+k/HaLG/Xl4N6ndUdnlj9JgY07M3dyv2rdHYRFJ9Nu/aGVkQb3A4UpAmNEUzOmCkcER9w/6kovXntUpROstO+8bD5VXjPwqoTwvdsW8bc3nkPCDwEU1wx0SqVbR97vNhXe9j90xxK+/LGFfPvGc/jyx9xopdsvrON7t/4eFd6NHMmMGIrOP9HXXUd7uPe5Laxqbg+UmeDWG/rWDefwn5+/iAvm1GREIuWbMtMKdz/ZyqbdXby4eV/ez02BPV3HOHNadd5xcZxbOz5rchWBu65r4JHPLeG2C+uKmtxvOG8GX/7YQu6+voGKEimDCkdy1oxLOMJfX9fAskvnZRy/5gPTgwCEh+5Ywm0X1JEQePaNPVk9MqB3wo/20AgvUgSYMm4UCrze3hkbDnxKZWZo09HIri7sHxhozDRkjFj8AnWptBuNcdd1DSycVh1rv31k2UWsam4P/gBvv7Aua+xV9dNyOv2i5gB/Bbhk3kQeW3YRK5vbgzjxZFoDc0d4Epo6blRGk5nvv9hGWl3bvl/+QnHt8nf9rJU/+uAcmrcddG3ETq+j8dXtB3lm456sycSfKJ5q2VVUg/XunjTtB3OXm5gWktcn4bg7rdZdLVnlOjqOdAefU8OM8XmL291w3gzuufX3Mo594ycb+m0iissYPrd2PEvPr+PuJ1vpSaZJOMJZ08f1VmBVpeNIN9WjKzPq+jz5+i7SujOIzpkxYTTJdHwXvZoxVTzVsiswxXX39Dpyl8ybmNE/I1+XOUfgtLFVSI5uZgmvxG6pkspMERgjFn/F5YfyhSeiOFZ6K+9VobIA0Ubhxdhe4xLc/FV9RcLh1gtmIcAjr2T6FfZ4E4EjIKFetsk0OJE//+5kmvtfbOttfp5WqkdX8oUrTqdpawcvbt4XTG6XL5zC85vchKjKCoerG6azNtLUJg7HEcafUkHXsezG86dUOtz7qcXuZxXyj8yddCqPrduWVXvJEQl2OEvmTWThtGquPGtq4GiPSnLgcGYlzYXTqnFiopnOn1PDa+2dBZsRxWUMv7HrEAunVWc4d8E1w0XDPUdVuhO2xLROjYYv+130esNCe0kTKRetuSPBKjxnsEim/yjc9B5cR/Hd1zUEzZus1pBhhIjLL8hFrs5n/SF6L38F7teSmTlhNEvmTQwUD/T2S3CAD50+idGViQzHYNz6PTwZOCGfRVw4bdPWjozXrTs7MybwKCJwx8VzAbhvdVvvcwRujWSxrgyZrnKF4SbTyn2r29xqsAkBEbeRUMJh6eJajhxP8virve1Grm7I7Eq7pu1AbDTTGVOrufH3anls3TZadx0iHdNfOBdJr1RDtFxJXNRPuEqov4MI56yEr8nMDchEgBbP1r+m7UDO8uQO7s5qxoTRvLb9YMbvQvh7F2Bp46ygFWapKKgIRGQM8P8Cdar6ORFZACxU1SdLKplhFKBQfkGYviiNQkTvdXXD9IySF9HJIzq5fOnKMwB4/i13VV/h+S/8JuquAxUcxyGZcuPPwz4L/73ne33Tolp+tH57VnmE+ZPH8s6BI6i6oYw3L6rNiNq57YK6rPLKlyyYTNPWDt47XLgevoJXr8ddCYcV48+9CJjKhLAw4ptYMm9ibCmQx9Zvx2E7ybRS4QhLL6yjelQFrbsOMXFsFevefS9ntdCs1TlkKcy4zy+XeTE83s8N8O3/vtQK/LipnZsX1Wb8niQSDpefMTlj5+ZH/3zjJxtyfp7+uFJTzI7g34Em4CLvdTvwI8AUgTHkFGvO6YvS6M+9Ck0ececf+VymycJXGr4JwD/WH3l9v8j9L7wdmGcSjjBv8qlBX4aeZJq9XcczTBfVo3qnhKatHdy24uWCJqYojldcLZlyTS1dR3u455m3AnNSOq2xO7KFU09l467MPgmplOIbrvwQzAdffjeja10+OcLNXKImvf5m50aVfMeR7oxVfTKVDnYi0XGXL5ySYeJ5eO02WnZ05uxpfF7teNa0HWDT7q4hNw3NV9WlInIbgKoeFfHbXBjGyKEv8d99vVehe8edj7tH3HXFEF3p+q8/f9l8Ll84heU/bSGtyvNv7aPCW3lXVjhMqR6VsaJd8WIbdRPHcvuFdW5fhyKUQFXCvZ+vbO6+vgHoLRnum4x8Z2zCEXYePErT1o5A1nAjIZ+s/AKB1h2deZWAf3+NcayGTTrdyTT3PPMWVzdMz5hgi1UW0e/u4bXbAkUQbivpj7nt+71+iUc+tyRQAl/PsxsA12/g+w4coWRN7ItRBN0iMhrvOxGR+cDQlgs0DCMgznntt/t0RPjwmVMCB2gqlebWC1zbtD9JPrZue2DLTit88/ENtO7spH7G+Kzm7adPHsvOzmMc6e4t3tPtmXuWNs4KfAt+HX2f8MStuI50v5Z/tJEQuCG9DTMzs6RTaQpmTY+qdN9/687sEM1wuYe0ulVdX9y8P2OC7a8vqeNId6C4om0l73/h7UDJ+cEKi2fX8Ni6viUplrInQTF5BH8F/BKYJSIPAc8C/2NApTAMo9/kc14n08qzb+wJOmP5Nmffgbp4dg13X9+QkReQUneFe/eTrXzuknkkvPyICge2dRzNUAI+PSmlNVRe2Z90oxOM4vlCIlE5fi6Gz+cumcfya+tj8wuE7CQxwQ13/cxFc1g4rZqVze08+sq2jLh+36TzodMnZeRjxEUI9bX+/5J5ExlV6V5XVdl7XdPWDp59c2/G2JYdnTy8dhsbdx0q6t6+rE4fZeoLBXcEqvq0iDQDSzyZ/kxV9xdzcxH5OPBPQAJ4QFW/Ezk/HvgPoM6T5e9V9d/79hYMo7yJc16//PaBYEWuCrcsrg2cttHVpB+R4tfDcV29bkx8665DfOuGc+g40s3Og0ezQmLDvNbeye/f9zu+dcM53H5hXbDCDtotptWd8GNq5pw9fVyw2heg63gyUFJhuRzciXb5NfW07OzksXXbg3O7Dx3nvtVtNG/rCMxH0RX04tk1fOnKMzLCa8MTbH99SbmuW9N2IKg26vN6eyctO4rLmbjhvBksmFqd4TsaEh+BiHwIeFVVfy4ifwB8XUT+SVW3FrguAdwLXIXrYF4nIk+o6sbQsC8AG1X1WhGZDGwSkYdUtXB4gmEYQO5JaPlPW0inlapKp2BTEz/BbmVzu9toJZkmDfx2y37WvfseD92xBMgMJY0j5ZmW/N66vi28fsY4pow7hSsWTqFlZyeCG9m0aXdXMNH7+Kaj6lEVVI+u5O7rG4K+1eHJ8Bs/2RBb+jvckjNcKiQDEQQl4Zm0wvV7+utLirsubI7yQ4jV+5wSjpBAMyKKelK9eQcOsGBqNV+44vQ+y9JXivER/CtwroicC3wF+AHwQ+CyAtddAGxR1TYAEXkUuB4IKwIFqj3n86nAe0B2dothGHmJTkJxmdPF3uPmRbXc88xb/HbL/gyziR8Fc88zb/Hi5txGgZS6CmPx7Bq+84s3QnkKnTz3phvBVFXhUD9jfGYP6hBpdfMb8jlIi1lRf7JxVtZ1vk9CgXRKmTFhdElW2ZCppKP5Ah85cwrnzpqQ4ahe1dzOj9ZvD5z5pWpNGaUYRZBUVRWR64Hvqeq/ich/LeK6mcD20Ot24MLImH8GngB2AtXAUlXNyhcRkWXAMoC6utImVhjGycKJrGy/dOUZWbkR/rmrG6bzuy37Mxr+qGaGPz76yjbGjapgxYttGff2fcK+L6NQM5+0wvGedKBYwty8qJbHXtlGvsCm+hnjs47VjKkKlEhcrsFA438PTVs7gtyRygqHz182Pzby7KY8/Q1KRTGKoEtE/gL4A+BSz+RTWcR1cSGm0a/sY8CrwIeB+cDTIvKiqmZ4UVR1BbACoLGxsb/lSAzDKJJc5qaH127zQlHd7OTzZ9ewqK4ma8JPK9z/YlvWX3yFpzTCiXjdPWlEcH0HMYohnKTly+GHx37uknmsWN2WM9u4JVLRE9yIHj9uP5prUEoWz67JyB3JNckPZJhzsRSjCJYCtwN/rKq7RaQO+LsirmsHZoVe1+Ku/MN8FviOut6ULSLyDnAm8EoR9zcMo4TExcpnFJJTaNp2MKPnQhjV3s5rIrDsknlZhf1889XOg0czej5I7yOA3iStaKx/hSNMGFuVM+s5qkBgYLPM+8pQTPLFUEzU0G7g/wu93obrIyjEOmCBiMwFdgC34iqUMNuAjwAvishUYCHQhmEYw4qmrR3BTiBMIdOOiOuviDpjfcJmk3A+A2RuJhzpdfpmJIalNG/pi7ACCT9zoLLMTxZyKgIR6SI7D2Q/8BzwVVXNWxRbVZMi8kXgV7jhoz9Q1VYRudM7fx/wLeBBEdmAuwj4arGhqYZhDB6rmttjnbqVoaziOFJFOmMXz67hjovnssKruFqRcHcS4Wxl/x7+ij6cYZyVhewRzvKNPs8UQC85FYGqZnWsEJEa4DPAfcAnC91cVX8B/CJy7L7QzzuBjxYvrmEYg03T1g5+tH571nGBQAkIMKNmNDs6jmYNKsb00rS1gwdffte9RFwFkqa3SdDtF9ZllNF46I4lQahrKuUWdRtVIXQdy052GywfwEimT2WoVbUD+EcR+XSJ5DEMY5iRq5yy0lsyWSFbCQCNRa68c5V2TqfdPhNxNYC+feM53LyollXN7Ty6bhtd8UVISx4VdDLQ51aVIlKJ9TEwjGFJXO/cEyUoF9HHUpMO8LWrz+rTM6KP8PswxNUAAtfEs6/reGwvA3B3KrYjKEw+H8FNMYdrcKOIflwyiQzD6BcDVWY5SrTs8r3Pb4ld/UdZdum8gs8Pm3uWX1PPNx/fEMpPyPQN5Ir0aY0JEfWpTOTILDYyyLeyvzbyWoEDwD+p6s9LJ5JhGP1hILuwRQlH9+w7lMMGE8IRqB6dP93ILz/tT+63LK7NcP4uPb+3M1e+SJ/RVdnT2OjKBBcvmMSdkaQtI56cpiFV/ayqfha3WNxnVfWPVPUrXs2hDw2ijIZhFEF/K2eGKWRaivoLHK8yaWVCuOrsqcHzq4p4vt/nWXHLM2/Z04UjgoNbTrphxvgMWRbPrslqOwnwRx+am3XvYz0pnntzL5t2d2WdM7Ipxtb/v4BFRRwzDGMIOdH4+GJMS9FkrOXX1Gc1din2+VH3c9PWDrfCqCN85qI5QU+FqpjnhK/pONLNDefNYPXm/fSkUnQdS7nlrtPK8p+2BAXwjNzk8xFcBHwQmCwifx46NQ43L8AwjGHGicTHF2NaKqRs+vL8mxfV8uP12+lJaVDywe1iprTuOpTRTczvsFbhCJ/0qoUCfOqBNUE+gSOZjd/BVQaFTGR9UV4nK/l2BFW4FUErcAvC+RwCbimlUIZhDD7FlF4YyElz8Wy3r7LvhL77ydaMngp+0TvxahApbibxw2vd7mY3LaoNTEsQ3/NXyJ/HUCoH+0gjX0LZC8ALIvJgod4DhmGMfAqt9ksxaYZ3ENGy2f5rX0n4K3+/4Yzg+iK6e9I5i86dWcAsVEoH+0iiGB/BAyLySVU9CEF28aOq+rGSSmYYxqCTz7RT6kkz+uyokghnEvstN/2SzTVjqnj8/7ZnNKUB+L0C8g1lAbrhRDGKYJKvBMDNLhaRKaUTyTCM4ciJTponYlYKN80J38OPKFo4rZrLFk7JUgQNMf0Iove1AnTFKYK0iNR5VUcRkdkU1xzIMIyTiBOZNAfarLRpd1dvW81UmoqEw2VnTCbh9PY0KDar2ArQFacIvgG8JCIveK8vxesWZhjGyUmu1Xt/J82BMCuFlUnUMdydTPP0xj0kxM1IVq9Xc7maevpKMf0Ifikii4AluEr2v1upaMM4eSmFU3ggbPG5CtOFSSlcddYUzgv1AjYKU2zxuBSwFzgFOFtEUNXVpRPLMIzBxt8F7Dx4dMCdwgNhi18ybyIVCYfuZK4YIZcp1aP4whWn91fUsqSgIhCRO4A/w201+SruzuBl3D7DhmGcBETbP1YknCA6Z6DMKydqi188u4ZbFtfyyNptOZ2UVV40kdE3itkR/BlwPrBGVa8QkTOBvy6tWIZhDCZhs0sqrSy9YBYzJ4weduYVv/+Am2gGiqCRjOPhJO9IoRhFcExVj4kIIjJKVd8UkYUll8wwjEEjasOPNnwfLkRNTEDZh34OBMUognYRmQA8DjwtIh3AzmJuLiIfB/4JtzbRA6r6nZgxlwP3AJXAflW9rJh7G4YxcIykePq4xDPjxBCNVmnKN1jkMmA88EtVzRugKyIJ4C3gKqAdWAfcpqobQ2MmAL8DPq6q20RkiqruzXffxsZGXb9+fdEyG4ZhGCAiTaraGHcu745ARBzgdVVtgKD+ULFcAGxR1TbvXo8C1wMbQ2NuB1b5yWqFlIBhGIYx8OTtWayqaeA1Eanrx71nAttDr9u9Y2HOAGpE5HkRaRKRP+zHcwzDMIwToBgfwXSgVUReAQ77B1X1ugLXxbW6jtqhKoDFwEeA0cDLIrJGVd/KuJHIMrxs5rq6/ugkwzAMIxfFKIJTgWtCrwX4bhHXtQOzQq9ryXYyt+M6iA8Dh0VkNXAurm8hQFVXACvA9REU8WzDMAyjSIpRBBVR34CIjC7iunXAAhGZC+wAbsX1CYT5KfDPIlKB2wjnQuAfi7i3YRiGMUDka1X5J8D/A8wTkddDp6qB3xa6saomReSLwK9ww0d/oKqtInKnd/4+VX1DRH4JvA6kcUNMW/r/dgzDMIy+kjN8VETGAzXA/wS+FjrVparvDYJssVj4qGEYRt/pV/ioqnYCncBtpRLMMAzDGHryho8ahmEYJz+mCAzDMMocUwSGYRhljikCwzCMMscUgWEYRpljisAwDKPMMUVgGIZR5pgiMAzDKHNMERiGYZQ5pggMwzDKHFMEhmEYZY4pAsMwjDLHFIFhGEaZY4rAMAyjzDFFYBiGUeaYIjAMwyhzTBEYhmGUOaYIDMMwypySKgIR+biIbBKRLSLytTzjzheRlIjcUkp5DMMwjGxKpghEJAHcC1wNnA3cJiJn5xj3XeBXpZLFMAzDyE0pdwQXAFtUtU1Vu4FHgetjxv0psBLYW0JZDMMwjByUUhHMBLaHXrd7xwJEZCZwI3BfvhuJyDIRWS8i6/ft2zfgghqGYZQzpVQEEnNMI6/vAb6qqql8N1LVFaraqKqNkydPHij5DMMwDKCihPduB2aFXtcCOyNjGoFHRQRgEvAJEUmq6uMllMswDMMIUUpFsA5YICJzgR3ArcDt4QGqOtf/WUQeBJ40JWAYhjG4lEwRqGpSRL6IGw2UAH6gqq0icqd3Pq9fwDAMwxgcSrkjQFV/AfwicixWAajqZ0opi2EYhhGPZRYbhmGUOaYIDMMwyhxTBIZhGGWOKQLDMIwyxxSBYRhGmWOKwDAMo8wxRWAYhlHmmCIwDMMoc0wRGIZhlDmmCAzDMMocUwSGYRhljikCwzCMMscUgWEYRpljisAwDKPMMUVgGIZR5pgiMAzDKHNMERiGYZQ5pggMwzDKnJIqAhH5uIhsEpEtIvK1mPOfEpHXvX+/E5FzSymPYRiGkU3JFIGIJIB7gauBs4HbROTsyLB3gMtU9QPAt4AVpZLHMAzDiKeUO4ILgC2q2qaq3cCjwPXhAar6O1Xt8F6uAWpLKI9hGIYRQykVwUxge+h1u3csF38MPBV3QkSWich6EVm/b9++ARTRMAzDKKUikJhjGjtQ5ApcRfDVuPOqukJVG1W1cfLkyQMoomEYhlFRwnu3A7NCr2uBndFBIvIB4AHgalU9UEJ5DMMwjBhKuSNYBywQkbkiUgXcCjwRHiAidcAq4NOq+lYJZTEMwzByULIdgaomReSLwK+ABPADVW0VkTu98/cBy4GJwL+ICEBSVRtLJZNhGIaRjajGmu2HLY2Njbp+/fqhFsMwDGNEISJNuRballlsGIZR5pgiMAzDKHNMERiGYZQ5pggMwzDKHFMEhmEYZY4pAsMwjDLHFIFhGEaZY4rAMAyjzDFFYBiGUeaYIjAMwyhzTBEYhmGUOaYIDMMwyhxTBIZhGGWOKQLDMIwyxxSBYRhGmWOKwDAMo8wxRWAYhlHmmCIwDMMoc0qqCETk4yKySUS2iMjXYs6LiHzPO/+6iCwqpTyGYRhGNiVrXi8iCeBe4CqgHVgnIk+o6sbQsKuBBd6/C4F/9f4fcOZ87efBz+9+57+U4hGGYRgjklLuCC4Atqhqm6p2A48C10fGXA/8UF3WABNEZPpACxJWAnGvDcMwyplSKoKZwPbQ63bvWF/HICLLRGS9iKzft2/fgAtqGIZRzpRSEUjMMe3HGFR1hao2qmrj5MmTB0Q4wzAMw6WUiqAdmBV6XQvs7MeYEybqEzAfgWEYRi8lcxYD64AFIjIX2AHcCtweGfME8EUReRTXSdypqrtKIYxN/oZhGPGUTBGoalJEvgj8CkgAP1DVVhG50zt/H/AL4BPAFuAI8NlSyWMYhmHEU8odAar6C9zJPnzsvtDPCnyhlDIYhmEY+bHMYsMwjDLHFIFhGEaZY4rAMAyjzDFFYBiGUeaI668dOYjIPmBrPy+fBOwfQHFKyUiR1eQcWEaKnDByZDU5XWaramxG7ohTBCeCiKxX1cahlqMYRoqsJufAMlLkhJEjq8lZGDMNGYZhlDmmCAzDMMqcclMEK4ZagD4wUmQ1OQeWkSInjBxZTc4ClJWPwDAMw8im3HYEhmEYRgRTBIZhGGXOSakIROTjIrJJRLaIyNdizouIfM87/7qILBqmcp4pIi+LyHER+fJQyOjJUUjOT3mf4+si8jsROXco5PRkKSTr9Z6cr3pd7y4ejnKGxp0vIikRuWUw5Qs9v9DnebmIdHqf56sisnw4yumNudyTsVVEXhhsGUNyFPpMvxL6PFu87/+0kgqlqifVP9yS128D84Aq4DXg7MiYTwBP4XZIWwKsHaZyTgHOB/4W+PIw/jw/CNR4P189FJ9nH2Q9lV7f2AeAN4ejnKFxv8Gt4HvLcJQTuBx4cii+7z7KOQHYCNR5r6cMV1kj468FflNquU7GHcEFwBZVbVPVbuBR4PrImOuBH6rLGmCCiEwfbnKq6l5VXQf0DLJsYYqR83eq2uG9XIPbaW4oKEbW99X7CwPGEtMadRAo5ncU4E+BlcDewRQuRLFyDjXFyHk7sEpVt4H7tzXIMvr09TO9DXik1EKdjIpgJrA99LrdO9bXMaVmOMhQDH2V849xd1tDQVGyisiNIvIm8HPgjwZJtjAF5RSRmcCNwH0MHcV+9xeJyGsi8pSI1A+OaBkUI+cZQI2IPC8iTSLyh4MmXSZF/z2JyBjg47iLgZJS0sY0Q4TEHIuu+ooZU2qGgwzFULScInIFriIYErs7Rcqqqj8BfiIilwLfAq4stWARipHzHuCrqpoSiRs+KBQjZzNuDZv3ReQTwOPAglILFqEYOSuAxcBHgNHAyyKyRlXfKrVwEfryd38t8FtVfa+E8gAnpyJoB2aFXtcCO/sxptQMBxmKoSg5ReQDwAPA1ap6YJBki9Knz1RVV4vIfBGZpKqDWZSsGDkbgUc9JTAJ+ISIJFX18UGR0KWgnKp6KPTzL0TkX4bp59kO7FfVw8BhEVkNnAsMtiLoy+/orQyCWQg4KZ3FFUAbMJdeZ0x9ZMx/IdNZ/MpwlDM09i6GzllczOdZh9t3+oMj4Ls/nV5n8SJgh/96OMkZGf8gQ+MsLubznBb6PC8Atg3HzxM4C3jWGzsGaAEahuNn6o0bD7wHjB0MuU66HYGqJkXki8CvcD30P1DVVhG50zt/H24UxidwJ68jwGeHo5wiMg1YD4wD0iLyJdwIg0O57jsUcgLLgYnAv3gr2KQOQRXFImW9GfhDEekBjgJL1fvLG2ZyDjlFynkL8CciksT9PG8djp+nqr4hIr8EXgfSwAOq2jKYchYrqzf0RuDX6u5gSo6VmDAMwyhzTsaoIcMwDKMPmCIwDMMoc0wRGIZhlDmmCAzDMMocUwSGYRhljikCY9ARkTkiMuihe3F4FSmf9H6+rkAl0PO87Nlc5xtF5HsFnvf1/kubcZ85InJ7nvMLRORJEXnbK6nwnJdJ7Z+/wavC+qaIbBCRG0LnHhSRd7zql80ictFAyGwMX0wRGIaHqj6hqt/JM+Q83PyTLESkQlXXq+p/K/CYAVEEwBzcQmpxspyCW0dpharOV9XFuAXs5nnnzwX+HrheVc8ErgP+3ssO9/mKqp4HfA24f4BkNoYppgiMoSIhIt/3asP/WkRGQ7DqXuOtVn8iIjXe8edF5B9FZLWIvOHV6V8lIptF5G/8m4rIH4jIK95q9n4RSUQf7NWDf1NEXgJuCh3/jIj8s/fzJ71a8K95z6wC7gaWevdeKiJ3icgKEfk18MPI7uJUEfl3b7X9uojcLCLfAUZ71z8UI9f7IvIP3ir8WRGZ7B0/XUSe8WRpFpH5wHeAS7x7/ffIrT4FvKyqT/gHVLVFVR/0Xn4Z+LaqvuOdewf4n8BXYr6n1bjZ2MZJjCkCY6hYANyrqvXAQdyMX4Af4hZb+wCwAfir0DXdqnopbkXOnwJfABqAz4jIRBE5C1gKfMhbzaZwJ8UAb7X8fdyCXpfglkiIYznwMVU9F7hO3ZLBy4HHVPU8VX3MG7cYd2UdXZ1/E+hU1XO89/IbVf0acNS7/lNkMxZoVtVFwAuh9/6Q91mdi9v7YRfuSv1F717/GLlPPW4xuFzUA02RY+u941Guxf0ejJMYUwTGUPGOqr7q/dwEzBGR8cAEVfW7R/1v4NLQNf4KdwPQqqq7VPU4bu2WWbiVJRcD60TkVe/1vMhzz/SevdkrhfAfOeT7LfCgiHwOtxRALp5Q1aMxx68E7vVfaG+/hnykAV/B/AdwsYhUAzPVrZiKqh5T1SNF3CvA21m1iMgq/xDxFXnDx/7O+wyX4VaUNU5iTrpaQ8aI4Xjo5xRuaeBir0lHrk/j/i4L8L9V9S8K3KdgXRVVvVNELsQtUPiqiJyXY2iuWjBxk21fUeLLFheilZACVdUbRaQR1y/gn2/Erbvjswi3g5fPV1T1x/14tjECsR2BMWxQ1U6gQ0Qu8Q59GtdEUizPAreIyBQAETlNRGZHxrwJzPXs7OB2gMpCROar6lpVXQ7sx91xdAHVRcrya+CLofvVeD/2iEhljmsc3CJu4DqCX/IKDLb7UT0iMkrchiX5ZHkY+JCIXBc6Nib0898DfyEic7x7zsF1Yv9DUe/MOOkwRWAMN/4rrlniddwonbuLvVBVNwJ/Cfzau/5pYHpkzDFcc8fPPWfx1hy3+zvP0duC6zB9DXgOONt3FhcQ529wO2K1iMhrwBXe8RXA63HOYtzdRb2INAEfpve9fxr4b957+h2uX+N1IOk5kDOcxZ6p6hrgThFpE5GXvc/lb7zzrwJfBX4mbqe2nwH/I2SqM8oMqz5qGMMEEXlfVU8dajmM8sN2BIZhGGWO7QgMwzDKHNsRGIZhlDmmCAzDMMocUwSGYRhljikCwzCMMscUgWEYRpnz/wOTmBCqSmUWtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.323 3.259\n",
      "fractional expected GOP seats =  8.071  out of  14 seats for NC\n",
      "simpler expected GOP seats =  8.1581  out of  14 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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